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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Environmental Monitoring Using Artificial Intelligence is a vital resource for anyone looking to leverage cutting-edge technologies in artificial intelligence and sensor systems to effectively address environmental challenges, offering innovative solutions and insights essential for creating a sustainable future.
Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability.
From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you’re exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Transformative Trends in AI for Environmental Monitoring: Challenges, Applications
1.1 Introduction
1.2 Literature Verticals
1.3 Key Methodologies in Literature Review
1.4 Most Common Methods in Environmental Monitoring
1.5 AI Architectures for Environmental Monitoring
1.6 Applications of AI in Environmental Monitoring
1.7 Challenges and Limitations of Using AI in Environment Modeling
1.8 Future Directions
1.9 Conclusion
Acknowledgements
References
2 Fundamentals of AI and NLP in Environmental Analysis
2.1 Introduction
2.2 AI and NLP Techniques
2.3 AI Models and NLP System with Data Science Cycle
2.4 Environmental Analysis Using AIoT and NLP
Bibliography
3 Smart Environmental Monitoring Systems: IoT and Sensor-Based Advancements
3.1 Introduction
3.2 Essential Elements and Factors for Environmental Monitoring with IoT
3.3 Diverse Avenues and Methodologies in IoT Environmental Applications
3.4 Conclusion
References
4 Remote Monitoring Advancements: A New Approach to Biodiversity Conservation
4.1 Introduction
4.2 Indicators of Primary Biodiversity
4.3 Exploring Biodiversity Conservation Strategies
4.4 AI Enhancing Animal Observation Images
4.5 AI and ML for Preserving Flora
4.6 Deep Learning Tracks Terrestrial Mammals via Satellites
4.7 Conclusion
References
5 Smart Water Solutions: A Case Study on Drone-Led Hydrological Investigation of Water Diversion from Lakshmiyapuram Catchment to Sivakasi Periyakulam Tank
5.1 Introduction
5.2 Software Used
5.3 Methodology
5.4 Conclusion and Recommendation
Acknowledgement
References
6 Sustainable Waste Management as a Key Feature for Smart City: A Case Study of Vadodara, Gujarat, India
6.1 Introduction
6.2 Material and Methodology
6.3 Result and Discussion
6.4 Limitation of Study
6.5 Conclusion and Future Prospects
References
7 Sensor Technologies for Environmental Data Collection
7.1 Introduction
7.2 Sensor Technologies
7.3 Background of Sensing
7.4 Types of Sensors
7.5 Applications of Sensors
7.6 Challenges of Sensors
7.7 Environmental Sensors
7.8 Summary and Recommendations
Bibliography
8 Significance and Advancement of Sensor Technologies for Environmental Analysis
8.1 Introduction
8.2 Sensing and Sensor Fundamentals
8.3 Key Sensor Technology Components
8.4 Regulations and Standards - Sensor Technologies
8.5 Conclusion
Bibliography
9 Texture-Based Classification of Organic and Pesticidal Spinach Using Machine Learning
9.1 Introduction
9.2 Related Works
9.3 Proposed Work
9.4 Implementation and Results
9.5 Conclusion
References
10 Deep Bidirectional LSTM for Emotion Detection through Mobile Sensor Analysis
10.1 Introduction
10.2 Literature Survey
10.3 Methodology
10.4 Results and Discussion
10.5 Conclusion
10.6 Future Directions
References
11 A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection
11.1 Introduction
11.2 Related Works
11.3 AlexNet
11.4 ResNet
11.5 Proposed Work
11.6 Conclusion
Acknowledgments
References
12 Comparison of Borewell Rescue L-Type Different Arm with Different Materials
12.1 Introduction
12.2 Related Works
12.3 Proposed Method
12.4 Cylinder
12.5 Ellipse
12.6 I-Beam
12.7 L-Angle
12.8 Mathematical Analysis
12.9 Results and Discussion
12.10 Conclusion
References
13 Optimizing Almond and Walnut Farming: A U-Net-Powered Deep Learning Approach for Energy Efficiency Prediction and Damage Assessment
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.4 Results and Discussion
13.5 Conclusion
References
14 Enhancing Sustainable Management of Waste Dump Sites with Smart Drones and Geospatial Tech: Air Quality Monitoring and Analysis
14.1 Introduction
14.2 Review of Relevant Literature
14.3 Methodological Framework
14.4 Outcomes and Discourse
14.5 Conclusion
References
15 Voltage Veggies: A Shocking Revolution in Agriculture
15.1 Introduction
15.2 Proposed Methodology
15.3 Experimental Approach
15.4 Conclusion and Future Research Directions
15.5 Conclusion
References
16 Emperor Penguin Optimized Loop Selection Process for Routerless NoC Design
16.1 Introduction
16.2 Related Works
16.3 Design of Routerless NoC
16.4 Emperor Penguin Optimized (EPO) Loop Selection
16.5 Result and Discussion
16.6 Conclusion
References
17 Case Study on Flyover Construction and the Air Quality Measurement by the Emission Level of Pollutants
17.1 Introduction
17.2 Related Study
17.3 Case Study on Flyover Construction and the Air Quality Measurement
17.4 Conclusion
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Common environmental monitoring methodologies.
Chapter 5
Table 5.1 Estimate of volume of water against rainfall.
Chapter 6
Table 6.1 India’s state-wise solid waste generation and its management (TPD).
Table 6.2 Survey questionnaire.
Chapter 7
Table 7.1 Sensor types.
Table 7.2 Threshold limit values for several air pollutants.
Table 7.3 WHO noise community noise guidance.
Table 7.4 EPA national primary drinking water standards for byproducts disinfe...
Table 7.5 EPA national primary drinking water standards for disinfections.
Chapter 9
Table 9.1 BP images and histograms obtained from the dataset.
Table 9.2 Intensities of texture patterns obtained by LBP.
Chapter 12
Table 12.1 Performance comparison of L-type A-I beam.
Table 12.2 Efficiency comparison.
Table 12.3 Deformation comparison.
Table 12.4 Stress comparison.
Chapter 14
Table 14.1 Air quality data comparison across various months and altitudes.
Chapter 15
Table 15.1 Literature survey on plant impulses.
Chapter 16
Table 16.1 Eight possible ways of loop.
Table 16.2 Comparison of EPO-based routerless NoC with existing techniques.
Chapter 17
Table 17.1 The length calculation of the Gounder Mills flyover bridge.
Table 17.2 The time taken for the travel distance due to humps and dumps on th...
List of Illustrations
Chapter 1
Figure 1.1 Convolution and LSTM model for environmental monitoring.
Figure 1.2 CNN model for vegetation monitoring.
Figure 1.3 Machine learning in environment modeling and prediction.
Figure 1.4 CNN model for forest change study.
Figure 1.5 DQN Architecture for environment modelling.
Figure 1.6 Transformer model for agricultural land monitoring.
Figure 1.7 F-RCNN model for environment modeling.
Figure 1.8 Ensemble learning for energy prediction.
Figure 1.9 Semantic IoT architecture.
Chapter 2
Figure 2.1 Perspective on the data science cycle.
Figure 2.2 An overview of environmental analysis using AIoT and NLP.
Chapter 3
Figure 3.1 SEM system monitors water contamination with IoT and sensors linked...
Figure 3.2 Smart farming employs digital tech for efficient and sustainable ag...
Figure 3.3 IoT facilitates air quality and traffic monitoring through regressi...
Figure 3.4 Smart system integrating real-time monitoring and advanced data man...
Figure 3.5 Urban intelligence and fundamental elements of smart city [10].
Figure 3.6 Arduino-based wildlife animal protection system with tracking and a...
Figure 3.7 Industrial automation [12].
Chapter 4
Figure 4.1 Organization of essential biodiversity indicators.
Figure 4.2 AI cameras detected tracks in the forest.
Figure 4.3 Counting animals with AI drones.
Figure 4.4 AI’s diverse roles in conserving forests and plants.
Figure 4.5 Satellite-based monitoring of diverse terrestrial mammal population...
Chapter 5
Figure 5.1 Boundary marking of points using GPS.
Figure 5.2 Drone flight planning over the study area.
Figure 5.3 Drone-captured images.
Figure 5.4 Orthomosaic map.
Figure 5.5 (a) Orthomosaic map for surface area measurements in the upstream o...
Figure 5.6 Orthomosaic map for whole area measurements.
Figure 5.7 Digital surface model of the study area.
Figure 5.8 Generated 3D model of the study area.
Figure 5.9 Estimate of cut and fill volume.
Figure 5.10 Coordinate measurement on the generated 3D model.
Figure 5.11 Contour map of the study area.
Figure 5.12 Contours overlayed on orthomosaic map.
Figure 5.13 Fill raster of the study area.
Figure 5.14 Flow direction.
Figure 5.15 Streamlines for the study area.
Figure 5.16 Stream order for the study area.
Figure 5.17 Water outlet along with its geographic location.
Figure 5.18 Water catchment area.
Figure 5.19 (a-d) Fill volume estimation of the study area.
Figure 5.20 Visualization of surface elevation.
Figure 5.21 Major outlet – geographic locations.
Figure 5.22 Establishing connections across the outlets.
Figure 5.23 Directions of the outlets.
Figure 5.24 Water outlet trace in the study area.
Chapter 6
Figure 6.1 Components of smart cities.
Figure 6.2 India yearly solid waste generation.
Figure 6.3 Situation of treated solid waste handling in India (%).
Figure 6.4 Management of solid waste landfills in India (%).
Figure 6.5 India’s solid waste management gap (%).
Figure 6.6 Location map of case study.
Figure 6.7 Outline of survey.
Figure 6.8 KSA foundation office, Sama, Vadodara.
Figure 6.9 Socio-demographic profile of respondents.
Figure 6.10 Awareness level and willingness of the respondents.
Figure 6.11 Outline of generalised waste management system.
Figure 6.12 Jambuva Landfill, Vadodara, Gujarat, India.
Figure 6.13 Outline of KSA waste management system.
Figure 6.14 (a) Weighing of dry waste (b) Soap given as an incentive.
Figure 6.15 (a) Bio-degradable garbage waste, (b) Bench, (c) Brick, (d) Pen, (...
Chapter 7
Figure 7.1 Evolutionary history of sensor technologies.
Figure 7.2 The sensing process.
Figure 7.3 Process of optical sensors.
Figure 7.4 The process of electrochemical sensors.
Figure 7.5 The process of biosensors.
Figure 7.6 Stand-off LIBS probe head, Laser ablation energy and spectroscopic ...
Figure 7.7 The 1 mm2 RadFET element fits on a standard TO-18 package header: O...
Figure 7.8 The 1 cm2 CZT array sits on a dip package on a circuit board for a ...
Figure 7.9 Four SAW sensor elements aligned vertically on an application speci...
Figure 7.10 Chemiresistor arrays developed at Sandia with four conductive poly...
Figure 7.11 Electric field gradients created between microfabricated posts sep...
Figure 7.12 A miniaturized biosensor is shown consisting of a shear horizontal...
Chapter 8
Figure 8.1 Sensor and transducer.
Figure 8.2 The high-level architecture of a sensor system.
Figure 8.3 Sensor platforms.
Chapter 9
Figure 9.1 Pesticide detection using LBP and SVM algorithms.
Figure 9.2 Organic texture intensities obtained using LBP histograms.
Figure 9.3 Pesticidal texture intensities obtained using LBP histograms.
Figure 9.4 Comparison of precision of LBP and existing models.
Figure 9.5 Comparison of accuracy of SVM and existing models.
Chapter 10
Figure 10.1 The captivating fusion of emotion recognition’s interdisciplinary ...
Figure 10.2 Integrating mobile sensor data with deep bidirectional LSTM for em...
Figure 10.3 Statistical analysis post-feature selection.
Chapter 11
Figure 11.1 AlexNet architecture [27].
Figure 11.2 ResNet architecture [28].
Figure 11.3 Loading images into the dataframe.
Figure 11.4 AlexNet.
Figure 11.5 Leaning Curve (Loss) and (Accuracy).
Figure 11.6 Confusion matrix of ResNet-5.
Figure 11.7 Learning Curve (Accuracy) for AlexNet.
Figure 11.8 Learning curve (accuracy) for ResNet152.
Figure 11.9 Learning Curve (Accuracy) for ResNet50.
Chapter 12
Figure 12.1 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.2 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.3 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.4 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.5 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.6 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.7 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.8 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.9 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.10 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.11 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.12 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.13 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.14 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.15 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.16 (a) Analysis of deformation. (b) Analysis of stress.
Figure 12.17 Accuracy analysis.
Chapter 13
Figure 13.1 Flowchart depicting the fourfold cross-validation modeling framewo...
Figure 13.2 Architecture of U-Net.
Figure 13.3 Optimizing almond and walnut farming: a U-net-powered deep learnin...
Figure 13.4 Energy-related indicators in the cultivation of almonds and walnut...
Figure 13.5 Contrasting energy inputs in almond and walnut production.
Figure 13.6 Measuring the energy expenditures in various cultivation circumsta...
Figure 13.7 A comparative analysis with ANN and U-Net - almond.
Figure 13.8 Comparative analysis of walnut with ANN and U-Net.
Chapter 14
Figure 14.1 Systematized data gathering model.
Figure 14.2 Identification of study area [16].
Figure 14.3 Field data collection with a touch of elegance.
Figure 14.4 Graph depicting variations at diverse altitudes across different m...
Figure 14.5 Air quality parameters display intriguing changes across months an...
Chapter 15
Figure 15.1 Flow process.
Figure 15.2 Work flow of plant impulse transfer from good plant to defective p...
Figure 15.3 Experimental results on plant growth.
Chapter 16
Figure 16.1 Routerless NoC design.
Figure 16.2 4 × 4 grid.
Figure 16.3 Better set of loop for 4 × 4 grid.
Figure 16.4 Flow chart of EPO-based routerless NoC design.
Figure 16.5 RTL view of EPO-based routerless NoC design.
Figure 16.6 Internal RTL schematic representation of EPO-based routerless NoC.
Figure 16.7 Simulation output of EPO-based routerless NoC.
Figure 16.8 Comparison analysis of flip flops.
Figure 16.9 Comparison analysis of LUTs.
Figure 16.10 Comparison analysis of slices.
Figure 16.11 Comparison analysis of IOB’s.
Figure 16.12 Comparison analysis of frequency.
Figure 16.13 Comparison analysis of power consumption.
Figure 16.14 Comparison analysis of latency.
Figure 16.15 Comparison analysis of average hop count.
Chapter 17
Figure 17.1 Airveda equipment.
Figure 17.2 Flyover construction and damages of Gounder Mills service road.
Figure 17.3 The pollution level from 7 a.m. to 9 a.m.
Figure 17.4 The pollution level from 9 a.m. to 11 a.m.
Figure 17.5 The pollution level from 11 a.m. to 1 p.m.
Figure 17.6 The pollution level from 1 p.m to 3 p.m.
Figure 17.7 The pollution level from 3 p.m. to 5 p.m.
Figure 17.8 The pollution level from 5 p.m. to 7 p.m.
Figure 17.9 The pollution level at 7 p.m.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
About the Editors
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Environmental Monitoring Using Artificial Intelligence
Edited by
A. Suresh
T. Devi
N. Deepa
and
Ali Kashif Bashir
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-27036-1
Front cover images supplied by Adobe FireflyCover design by Russell Richardson
Preface
In this book, readers are invited to explore a diverse array of topics centered around the theme of environmental monitoring and technological innovation. From transformative trends in AI for environmental monitoring to the utilization of advanced sensor technologies, each chapter offers a deep dive into the cutting-edge techniques and applications driving progress in this field. Through insightful discussions and case studies, insights into how these tools enable more precise and efficient tracking of environmental changes, from climate patterns to biodiversity dynamics, are revealed. Furthermore, the role of smart environmental monitoring systems, IoT advancements, and remote sensing technologies in providing real-time data on environmental conditions is explored, paving the way for smarter, more resilient communities and ecosystems.
Moreover, the integration of AI, Natural Language Processing (NLP), and sensor technologies facilitates data-driven decision-making, enhances resource efficiency, and promotes sustainability in various sectors, from agriculture to urban planning. Each chapter offers a glimpse into the future of environmental monitoring, where innovative technologies pave the way for smarter, more resilient communities and ecosystems. Join us as this exploration navigates through these chapters, uncovering the remarkable potential of AI, NLP, and sensor technologies in safeguarding our environment and shaping a more sustainable future for generations to come.
Chapter 1: Transformative Trends in AI for Environmental Monitoring: Challenges, Applications involves leveraging advanced data analytics, machine learning, and remote sensing to enhance the precision and efficiency of tracking environmental changes. These technologies enable the monitoring of climate change, pollution, and biodiversity in real time, offering predictive insights that facilitate proactive environmental management. Key applications include automated wildlife tracking, optimizing renewable energy deployment, and detecting deforestation. However, several challenges impede widespread adoption. High computational costs and the need for extensive, high-quality data are significant technical barriers. Data privacy and ethical concerns also pose challenges, as the extensive data collection required can infringe on individual privacy rights. Additionally, the deployment of AI systems in remote or underdeveloped regions can be difficult due to infrastructure limitations. Finally, despite these challenges, the integration of AI in environmental monitoring promises substantial benefits, enhancing our ability to protect natural resources and respond to environmental threats more effectively. Addressing these challenges is crucial for maximizing AI’s potential in this field.
Chapter 2: Fundamentals of AI and NLP in Environmental Analysis involve using advanced algorithms and machine learning techniques to process and interpret vast amounts of environmental data. AI models can analyze patterns, predict trends, and make data-driven decisions, enabling more accurate and efficient environmental monitoring and management. Natural Language Processing (NLP), a subfield of AI, plays a crucial role in environmental analysis by processing and analyzing textual data from diverse sources such as research papers, policy documents, and social media. NLP techniques can extract relevant information, identify trends, and summarize large volumes of text, aiding in the synthesis of environmental knowledge. Together, AI and NLP facilitate the integration of multiple data sources, from satellite imagery to sensor networks and textual data, providing comprehensive insights into environmental conditions. They enable automated monitoring, early warning systems for natural disasters, and informed policy-making. Finally, the integration of these technologies enhances our capacity to understand and address complex environmental challenges.
Chapter 3: Smart Environmental Monitoring Systems: IoT and Sensor-Based Advancements provide real-time, accurate data on environmental conditions. IoT devices, interconnected through networks, collect data from various sensors measuring parameters such as air and water quality, temperature, humidity, and soil moisture. These sensors are often deployed in remote or inaccessible areas, continuously transmitting data to centralized systems for analysis. Advancements in sensor technology have led to the development of highly sensitive, low-power sensors capable of detecting minute changes in environmental conditions. These sensors, combined with IoT, enable continuous monitoring and rapid response to environmental changes, improving the ability to predict and mitigate environmental hazards. The integration of IoT and sensors in SEMS facilitates better resource management, pollution tracking, and disaster preparedness. These systems support data-driven decision-making, enhancing the ability to protect and sustain natural resources. Finally, the ongoing improvements in sensor accuracy, energy efficiency, and connectivity are driving the evolution of more sophisticated and responsive environmental monitoring solutions.
Chapter 4: Remote Monitoring Advancements: A New Approach to Biodiversity Conservation by utilizing cutting-edge technologies like drones, satellite imagery, and automated camera traps. These tools provide continuous, non-invasive monitoring of wildlife and habitats, offering detailed insights into species distribution, behavior, and population dynamics. Drones equipped with high-resolution cameras and sensors can access remote or difficult-to-reach areas, capturing real-time data on flora and fauna. Satellite imagery enables large-scale environmental monitoring, tracking changes in land use, deforestation, and habitat fragmentation over time. Automated camera traps, with AI integration, can identify and track species, reducing human intervention and minimizing disturbance to wildlife. These remote monitoring technologies enhance the accuracy and scope of biodiversity data collection, facilitating timely and informed conservation strategies. They enable early detection of threats, such as poaching or habitat degradation, and support the implementation of targeted conservation measures. Finally by providing comprehensive and precise data, remote monitoring advancements are crucial for effective biodiversity conservation in an era of rapid environmental change.
Chapter 5: Smart Water Solutions: A Case Study on Drone-Led Hydrological Investigation of Water Diversion from Lakshmiyapuram Catchment to Sivakasi Periyakulam Tank involves drones equipped with high-resolution cameras and LiDAR sensors that were deployed to assess and map the water diversion pathways and catchment characteristics. The drones collected detailed aerial imagery and topographical data, enabling precise mapping of the catchment area and water flow patterns. This data was used to identify potential inefficiencies and blockages in the diversion channels, as well as to monitor the water levels and storage capacity of the Sivakasi Periyakulam Tank. The drone-led investigation provided a comprehensive understanding of the hydrological dynamics, facilitating informed decision-making for improving water management and distribution. The use of drones significantly reduced the time and cost associated with traditional ground-based surveys, demonstrating the effectiveness of smart water solutions in enhancing the efficiency and sustainability of water resource management.
Chapter 6: Sustainable Waste Management as a Key Feature for Smart City where smart cities integrate advanced technologies to create sustainable environmental solutions, enhancing urban living while minimizing ecological footprints. Utilizing IoT, big data, and AI, smart cities monitor and manage resources efficiently, addressing challenges like pollution, waste, and energy consumption. Key solutions include smart grids that optimize energy distribution, reducing wastage and incorporating renewable sources. Intelligent transportation systems alleviate traffic congestion and lower emissions through real-time data analysis and adaptive traffic management. Waste management is revolutionized by smart bins and recycling systems that monitor fill levels and optimize collection routes. Water management benefits from sensors that detect leaks and monitor usage, ensuring efficient distribution and conservation. Air quality is continuously monitored, enabling prompt responses to pollution and health risks. Green infrastructure, such as vertical gardens and green roofs, enhances urban biodiversity and mitigates heat islands. Finally by integrating these technologies, smart cities promote sustainability, improving quality of life and fostering resilience against environmental challenges.
Chapter 7: Sensor Technologies for Environmental Data Collection where sensor technologies are essential for accurate environmental data collection, covering a wide range of parameters. Air quality sensors measure pollutants like particulate matter, carbon monoxide, and ozone, crucial for monitoring urban pollution and health impacts. Water quality sensors track pH, dissolved oxygen, and contaminants, aiding in aquatic ecosystem health and safe drinking water management. Soil moisture sensors optimize agricultural irrigation, improving crop yields and water conservation. Temperature and humidity sensors provide critical data for weather forecasting, climate studies, and environmental control systems. Remote sensing technologies, including satellite and aerial sensors like LiDAR and multispectral cameras, offer large-scale monitoring of deforestation, land use, and topographical changes. Acoustic sensors detect wildlife sounds and human activities, assisting in biodiversity conservation and poaching prevention. Integrating these sensors into IoT networks allows for realtime data collection and analysis, supporting informed decision-making and sustainable environmental management. Future advancements aim to enhance sensor durability, reduce costs, and improve data integration.
Chapter 8: Significance and Advancement of Sensor Technologies for Environmental Analysis where sensor technologies are vital for precise environmental data collection across various domains. Air quality sensors measure pollutants such as particulate matter, carbon monoxide, and ozone, essential for monitoring urban air quality and health impacts. Water quality sensors track parameters like pH, dissolved oxygen, and contaminants, aiding in the management of aquatic ecosystems and ensuring safe drinking water. Soil moisture sensors are crucial for agriculture, optimizing irrigation and improving crop yields by measuring soil water content. Temperature and humidity sensors provide key data for weather forecasting, climate research, and environmental control systems. Remote sensing technologies, including satellites and aerial sensors like LiDAR and multispectral cameras, enable large-scale monitoring of deforestation, land use, and topographical changes. Acoustic sensors detect sounds from wildlife and human activities, supporting biodiversity conservation and anti-poaching efforts. Integrating these sensors into IoT networks allows real-time data collection and analysis, facilitating informed decision-making for sustainable environmental management.
Chapter 9: Texture-Based Classification of Organic and Pesticidal Spinach Using Machine Learning to differentiate between these two categories based on textural features extracted from images. Machine learning models, such as convolutional neural networks (CNNs) or support vector machines (SVMs), are trained on datasets containing images of both organic and pesticidal spinach. The texture features extracted from these images capture subtle differences in visual patterns, such as leaf surface texture, color variations, and structural characteristics. These features serve as input to the machine learning model, which learns to classify spinach samples into organic or pesticidal categories based on the extracted texture information. By leveraging machine learning for texture-based classification, this approach offers a non-destructive and efficient method for distinguishing between organic and pesticidal spinach, aiding in quality control and ensuring consumer safety in the agricultural industry.
Chapter 10: Deep Bidirectional LSTM for Emotion Detection through Mobile Sensor Analysis employs a sophisticated neural network architecture to analyze data collected from sensors in mobile devices. Bidirectional LSTMs enable the model to capture temporal dependencies in the sensor data by processing it both forwards and backwards through time. The deep architecture incorporates multiple LSTM layers, allowing the model to learn complex patterns and representations from the sequential sensor data. By leveraging bidirectional processing and deep learning techniques, the model can effectively capture nuanced patterns indicative of different emotional states. This approach enables mobile devices to infer users’ emotions in real-time based on sensor data, such as accelerometer or gyroscope readings. Applications include personalized user experiences, mental health monitoring, and emotion-aware interfaces, enhancing human-computer interaction and emotional well-being.
Chapter 11: A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection involves evaluating the performance of two convolutional neural network (CNN) architectures in accurately identifying pneumonia from medical images, such as chest X-rays. AlexNet, a pioneering CNN model, consists of several convolutional and pooling layers followed by fully connected layers. ResNet, on the other hand, introduces residual connections, which help mitigate the vanishing gradient problem and enable training of deeper networks. The analysis involves training and testing both models on a dataset of chest X-ray images labeled for pneumonia presence. Metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) are typically used to assess performance. Results from the comparative analysis provide insights into the effectiveness of each model for pneumonia detection, aiding in the selection of the most suitable architecture for medical image analysis tasks.
Chapter 12: Comparison of Borewell Rescue L-Type Different Arm with Different Materials involves evaluating the performance of rescue tools designed for retrieving individuals trapped in borewells. These tools typically feature an L-shaped arm mechanism constructed from various materials such as steel, aluminum, or composite materials. The comparison assesses factors such as strength, durability, weight, and cost-effectiveness of each arm material. Steel arms offer high strength and durability but may be heavy and prone to corrosion. Aluminum arms are lightweight but may sacrifice some strength. Composite materials offer a balance between strength, weight, and corrosion resistance but may be more expensive. The evaluation considers the effectiveness of each arm material in practical rescue scenarios, including ease of deployment and manipulation in confined spaces. Ultimately, the comparison aims to identify the most suitable arm material that maximizes performance and safety in borewell rescue operations.
Chapter 13: Optimizing Almond and Walnut Farming: A U-Net-Powered Deep Learning Approach for Energy Efficiency Prediction and Damage Assessment involves predicting energy efficiency and assessing damage using a U-Net-powered deep learning approach. The U-Net architecture is utilized to analyze aerial imagery and predict energy efficiency by identifying areas with potential damage or inefficiencies in irrigation or pest control. This deep learning model processes satellite or drone imagery to detect signs of stress or damage in orchards, such as water stress, pest infestations, or disease outbreaks. By accurately predicting energy efficiency and assessing damage, farmers can optimize resource allocation, prioritize interventions, and minimize crop losses. The U-Net -powered approach offers a precise and efficient method for monitoring orchard health, enabling timely interventions to improve energy efficiency and mitigate damage. By leveraging deep learning and remote sensing technologies, almond and walnut farmers can enhance sustainability, productivity, and resilience in their farming practices.
Chapter 14: Enhancing Sustainable Management of Waste Dump Sites with Smart Drones and Geospatial Tech: Air Quality Monitoring and Analysis involves smart drones equipped with sensors flying over dump sites, collecting real-time data on air pollutants like methane, carbon dioxide, and volatile organic compounds (VOCs). Geospatial technology integrates drone data with geographical information systems (GIS), providing spatial context for air quality measurements. This enables precise mapping of pollution hotspots and identification of areas with elevated health risks. The data collected by smart drones and analyzed using geospatial techniques facilitate proactive management strategies, such as implementing gas collection systems, optimizing waste disposal practices, and mitigating environmental impacts. By monitoring air quality in real time and analyzing spatial patterns, waste dump site managers can make informed decisions to minimize pollution and protect public health while promoting sustainable waste management practices.
Chapter 15: Voltage Veggies: A Shocking Revolution in Agriculture where Voltage Veggies represents a revolutionary approach to agriculture by harnessing the power of electricity to enhance plant growth and productivity. This innovative technique involves applying controlled electrical currents to soil or plants, stimulating nutrient uptake, improving water efficiency, and boosting overall crop yield. By optimizing plant metabolism and root development, Voltage Veggies can accelerate plant growth, shorten crop cycles, and increase resilience to environmental stressors like drought or disease. Additionally, this method may reduce the need for chemical fertilizers and pesticides, promoting sustainable farming practices. Voltage Veggies holds promise for addressing global food security challenges by maximizing agricultural productivity while minimizing resource inputs and environmental impacts. As research and development in this field advance, Voltage Veggies could revolutionize the way crops are cultivated, ushering in a new era of electrified agriculture.
Chapter 16: Emperor Penguin Optimized Loop Selection Process for Routerless NoC Design where the Emperor Penguin Optimized Loop Selection Process (EPO-LSP) is a novel algorithm inspired by the behaviors of emperor penguins for designing routerless Network-on-Chip (NoC) architectures. In this approach, the movement patterns of emperor penguins, which involve forming loops to maintain communication and conserve energy in harsh environments, are emulated to optimize the selection of loops in NoC designs. EPO-LSP algorithmically determines the most efficient paths for data transmission within an NoC without relying on traditional routers. By leveraging the natural behaviors of emperor penguins, it aims to minimize energy consumption, latency, and resource usage while maximizing throughput and fault tolerance. This innovative approach offers a promising solution for designing efficient and robust NoC architectures, particularly in resource-constrained and energy-sensitive environments. By mimicking nature’s strategies, EPO-LSP demonstrates the potential for bio-inspired algorithms to address complex engineering challenges with elegance and efficiency.
Chapter 17: Case Study on Flyover Construction and the Air Quality Measurement by the Emission Level of Pollutants where in a case study on flyover construction, air quality measurements were conducted to assess the emission levels of pollutants during the construction process. Monitoring stations were strategically placed near the construction site and in surrounding areas to capture data on pollutants such as particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), and volatile organic compounds (VOCs). The study involved continuous monitoring of air quality before, during, and after construction activities. Emission sources, such as heavy machinery, vehicles, and material transportation, were identified and quantified to evaluate their impact on air quality. By analyzing the emission levels of pollutants, the study provided insights into the environmental impact of flyover construction and identified potential mitigation measures to minimize air pollution. This information is crucial for informing regulatory decisions, implementing pollution control measures, and safeguarding public health in urban areas undergoing infrastructure development.
The editors thank the contributors most profoundly for their time and effort.
Dr. A. Suresh
Dr. T. Devi
Dr. N. Deepa
Dr. Ali Kashif Bashir
1Transformative Trends in AI for Environmental Monitoring: Challenges, Applications
Leena Sri R.1, Divya Vetriveeran2*, Rakoth Kandan Sambandam2, Jenefa J.2 and Karthikeyan Thangavel3
1Thiagarajar College of Engineering, Madurai, India
2CHRIST (Deemed to be) University, Kengeri, Bangalore
3University of Technology and Applied Sciences, Al Khuwair, Muscat, Oman
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) is reshaping environmental monitoring, responding to the escalating complexities of issues like climate change and pollution. This article presents a comprehensive overview of current trends, challenges, and applications in AI-driven environmental monitoring. While technologies like remote sensing and Internet of Things (IoT) have improved data resolution, the sheer volume necessitates AI for efficient processing. The review emphasizes the role of AI in real-time monitoring, providing timely insights critical for addressing natural disasters and pollution. Exploring various environmental monitoring verticals—air and water quality, climate change modeling, biodiversity, and disaster prediction—the article highlights AI’s versatility in addressing diverse concerns. Challenges such as data quality, bias, interpretability, and privacy are examined, underlining ethical considerations in biased models impacting marginalized communities. This chapter discusses common environmental modeling methodologies, ranging from empirical to geospatial modeling, elucidating their advantages and challenges.
Keywords: Artificial intelligence, environment monitoring and modeling, deep learning models, AI challenges, research directions
1.1 Introduction
Environmental monitoring is a vital practice that involves systematically collecting and analyzing data to track changes in the environment. This process plays a crucial role in early pollution detection, enabling timely intervention to prevent widespread environmental damage. Additionally, it supports sustainable resource management by providing insights into the availability and quality of natural resources like water. Biodiversity conservation benefits from ecosystem monitoring, helping assess the health of various species and their habitats. Environmental monitoring also contributes to tracking climate change, aiding scientists in understanding longterm trends and making predictions. Moreover, it directly impacts public health by assessing air and water quality, identifying potential risks associated with pollution. Regulatory compliance is another key aspect, ensuring industries adhere to environmental standards. This practice generates valuable data for research, educates the public about environmental issues, and fosters a sense of responsibility towards nature, ultimately contributing to a balanced and sustainable coexistence with the planet.
Need for technology in environmental monitoring
The realm of environmental monitoring confronts significant challenges driven by the growing complexity of environmental issues, such as climate change and pollution. Conventional monitoring methods often struggle to deliver real-time, comprehensive data in the face of these dynamic and expansive challenges. The need for more accurate and nuanced information has become paramount, demanding the integration of advanced technologies. Technologies like remote sensing and satellite imagery offer a broader, more detailed perspective, addressing the challenge of better spatial and temporal resolution. Moreover, the sheer volume of data generated requires sophisticated tools like big data analytics and artificial intelligence (AI) to automate processing, identify patterns, and predict trends.
Real-time monitoring, facilitated by advanced sensor networks and Internet of Things (IoT) technologies, emerges as a critical solution to address environmental challenges promptly. These innovations enable continuous data collection and transmission, providing timely insights into changing conditions. This proves invaluable in responding to sudden events such as natural disasters or pollution incidents. However, the need for improved accessibility and affordability of these advanced monitoring technologies remains a crucial aspect. Making these tools more widely available ensures that communities, regardless of economic status, can actively participate in environmental stewardship, promoting a more inclusive and effective approach to environmental management.
Need for AI to solve these challenges
Machine learning (ML) and artificial intelligence (AI) stand as crucial pillars in addressing the multifaceted challenges of environmental monitoring. Their capacity to handle vast datasets efficiently is pivotal, unraveling intricate patterns and correlations that elude traditional methods. This capability is instrumental in gaining profound insights into complex environmental processes, facilitating a more nuanced understanding of factors at play. Additionally, the predictive modeling prowess of ML and AI empowers environmental scientists to anticipate and prepare for future challenges, whether related to climate change impacts or the likelihood of pollution events. By leveraging historical data, these technologies contribute to a proactive approach, enhancing the efficacy of strategies for environmental management.
Furthermore, ML and AI enhance the precision of data interpretation, a critical aspect in environmental monitoring where accuracy is paramount. Their ability to distinguish relevant signals from noise ensures more reliable monitoring results. This is particularly beneficial in addressing challenges associated with the demand for better spatial and temporal resolution. Equally significant is their role in real-time monitoring, as these technologies enable automated analysis of streaming data, ensuring the swift detection of abnormalities or shifts in environmental conditions. In summary, the integration of ML and AI into environmental monitoring processes significantly advances the field, offering improved data processing, predictive capabilities, enhanced accuracy, and real-time responsiveness to environmental challenges.
1.2 Literature Verticals
There are several verticals in which the monitoring had been done. Some of them include the following:
Air quality monitoring
Machine learning algorithms analyze air quality data, predicting pollutant levels and identifying sources of pollution. This aids in developing more effective air quality management strategies.
Satellite imagery and remote sensing
AI has been utilized to analyze satellite imagery for environmental monitoring purposes. This includes applications such as deforestation detection, land cover classification, and monitoring changes in ecosystems.
Water quality assessment
Machine learning models have been applied to assess water quality parameters, including the detection of contaminants in rivers and lakes. These models can provide real-time insights, aiding in the management of water resources.
Climate change modeling
AI and machine learning are used to analyze climate data, predict future climate trends, and assess the impact of climate change on ecosystems. This includes the development of models to understand complex climate dynamics.
Biodiversity monitoring
Researchers have explored the use of machine learning for biodiversity conservation. This includes species identification through image and sound analysis, as well as monitoring the health of ecosystems.
Natural disaster prediction and response
AI algorithms are applied to analyze data related to natural disasters, such as hurricanes, earthquakes, and wildfires. These models help in predicting the occurrence of disasters and planning effective response strategies.
Noise pollution monitoring
Machine learning has been employed to monitor and analyze noise pollution in urban areas. This involves the use of sensors and algorithms to assess noise levels and identify sources of excessive noise.
Integration of IoT devices
The combination of AI and the Internet of Things (IoT) devices is explored for real-time monitoring. This includes the deployment of sensor networks to collect environmental data, which is then analyzed using machine learning algorithms.
1.3 Key Methodologies in Literature Review
Air quality prediction
The paper by [1] shows that non-linear machine learning models, such as Random Forest and Support Vector Regression, effectively predict air quality using meteorological and traffic data, potentially reducing the need for air pollution sensors. This study by [2] proposes an AI-based framework for air quality prediction, aiming to achieve the best precision by utilizing controlled solicitation AI calculations and vehicle traffic office datasets. The research by the authors [3] shows that an AI-based system effectively predicts air pollutants in urban environments, aiding in shortterm measures to improve air quality and life quality.
The novel optimal-hybrid model by the authors [4], effectively predicts air quality index (AQI) using secondary decomposition, AI methods, and optimization algorithms, providing accurate and reliable information for environmental and public health. The study by the authors [5] shows that Deep Learning models are the most suitable for air quality classification in IoT-based networks with low-cost sensors, providing accurate results for public presentation.
Source identification of air pollutants using AI
Based on the research by the authors [6], artificial neural network (ANN) is the most accurate machine learning algorithm for identifying indoor pollutant sources, with ANN outperforming support vector machine (SVM) and k-nearest neighbor (KNN) in detecting release masses. There had been researches wherein the monitoring has been made efficient, and one such methodology was proposed by the authors [7]. This real-time calculation platform using Flink and Automatic Identification System (AIS) data can improve the estimation of ship air pollutant emissions, supporting blue-sky defense war and improving air pollution monitoring in water transportation.
There have been several research studies using satellite imaging that helps in proper monitoring of pollution in major cities. One such research is by the authors [8]. The F-RCNN-based aircraft identification framework accurately detects airplanes in satellite images, improving air pollution source identification in smart cities with a detection rate of 92%.
Predictive modelling of pollutant levels with machine learning
There has been research on several machine learning algorithms that are under research that helps in the ease of prediction and monitoring pollution risk. One such research by the authors [9] reports that, the KNN-DNN model, combining K-Nearest Neighbor (KNN) and Deep Neural Network (DNN), effectively improves fine-grained air quality analysis by using unlabeled samples and utilizing input variables like temperature, humidity, pollutants, and source type. There has been research on improving the time taken for the analysis, and one such research by the authors [10] has the following promising results. Our Deep Q-network-based UAV Pollution Tracking (DUPT) solution can rapidly identify unhealthy polluted areas and saves 28% of the total time compared to existing solutions. The authors [12] have researched the use of IoT for air quality classification using sensors.
Deep learning applications in air quality monitoring
With the rise in deep learning algorithms, there have been several research studies in the field of air quality monitoring too. One such research by the authors [11] shows the following results: Deep features extracted by CNN and fed to an extreme learning machine can accurately predict air pollution levels in smartphones, with a 66.92% accuracy compared to other conventional methods. Deep Learning models are the most suitable for air quality classification in IoT-based networks with low-cost sensors, providing accurate results for public presentation. There have been several research studies on convolutional neural networks and one such research by authors [12] shows that the proposed Convolutional Neural Network–based approach efficiently detects anomalous episodes in urban ozone maps, compared to other deep learning methods. The authors [13] proposes an ensemble approach for Multi-Source Transfer Learning, which improves air quality prediction by transfer learning from multiple source to a target, overcoming data shortage issues.
AI for environmental monitoring
The paper by the authors [14] recommends a greater use of satellite imagery and data in the enforcement of environmental, social and governance goals, but it does not specifically mention AI applications in satellite imagery analysis for environmental monitoring. The paper by the authors [15] discusses the objective selection of AI applications for onboard deployment in satellite missions, including the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME). It does not specifically mention AI applications in satellite imagery analysis for environmental monitoring. There have also been researches that help in building datasets for environmental monitoring. The paper [16] discusses an automatic tool for creating datasets for AI applications in the Earth Observation context, but it does not specifically mention AI applications in satellite imagery analysis for environmental monitoring.
Deforestation detection as a part of environmental monitoring using AI
Satellite-based deforestation detection using deep learning has been explored in several research papers. Fodor and Conde introduced a deep learning–based approach using Convolutional Neural Network (CNN) and multimodal satellite imagery to estimate deforestation and detect wildfires in the Amazon forest [17] Solórzano et al. evaluated that utilizing multispectral and synthetic aperture radar images, the U-Net 3D spatio-temporal deep learning algorithm demonstrates significant potential in detecting deforestation within a tropical rainforest in Southeast Mexico [18]. Ankalkoti presented a deep learning–based approach for the early detection of forest wildfires from satellite images, employing data preprocessing, model training, and inference steps [19]. Kang et al. proposed a deep learning–based forest fire detection algorithm using high temporal resolution satellite images, achieving better performance than traditional methods [20]. Wang et al. used a deep learning method to detect deforestation in large-scale scenarios based on high-resolution optical remote sensing images, achieving accurate results in detecting deforestation and capturing the changing area’s boundary [21].
AI and machine learning in climate change impact assessment
AI and machine learning techniques are being increasingly used in climate change impact assessment. These methods offer the potential to accurately model and predict the future of the climate system, aiding in our understanding of climate change and its effects [22]. Machine learning algorithms, such as artificial neural networks, decision tree modeling, and regression analysis have been applied to evaluate the effects of climate change on crop production [23][24]. These techniques can integrate information from high-dimensional datasets and provide timely and accurate forecasts of crop production, which are crucial for policy assessments and global food security [25]. In addition, machine learning has been used to analyze spatio-temporal dynamics of risk factors, assess multi-hazard interactions, and analyze future scenarios under climate change [26]. These applications have shown promising results in improving climate change risk assessment and supporting decision-making processes.
1.4 Most Common Methods in Environmental Monitoring
There have been several intelligent methodologies for several environmental monitoring techniques. There have been some classic methodologies in the literature that have their own advantages of implementations beyond the limitations. Some of those methodologies in literature are listed below in Table 1.1.
1.5 AI Architectures for Environmental Monitoring
Several architectures and frameworks have been developed to enhance the effectiveness of environmental monitoring using artificial intelligence (AI). These architectures leverage various machine learning and deep learning techniques to address specific challenges in environmental monitoring. Here are some notable architectures:
LSTMs, a type of recurrent neural network (RNN), are effective for time series analysis in environmental monitoring. They excel at capturing temporal dependencies and have been applied to predict air quality, water quality, and other environmental parameters. The latest research by the authors [39] has developed an architecture for the advance prediction of coastal groundwater levels. The architecture developed by the authors are as follows in Figure 1.1.
Convolutional Neural Networks (CNN) for image analysis
CNNs are widely used for analyzing satellite imagery and remote sensing data in environmental monitoring. These networks can identify patterns, detect changes in land cover, monitor deforestation, and assess the impact of urbanization on ecosystems. The authors [40] have used the CNN architecture for the vegetation monitoring in Karnataka, India, and the architecture is given in Figure 1.2.
Random forest and Support Vector Machines (SVM) for air quality prediction
Ensemble methods like Random Forest and SVM have been employed for air quality prediction. These models combine the strengths of multiple algorithms, providing accurate predictions based on meteorological and traffic data. The authors [41] have implemented the machine learning model for a better preprocessing, and the proposed architecture is as given in Figure 1.3.
Hybrid models for integrated monitoring
Hybrid models combining different machine learning approaches, such as the integration of neural networks with optimization algorithms, have shown effectiveness in predicting air quality indices. These models leverage the strengths of various techniques for improved accuracy.
U-Net 3D for deforestation detection
U-Net 3D, a spatio-temporal deep learning algorithm, has been applied to detect deforestation in tropical rainforests. This architecture considers both spatial and temporal dimensions, enhancing the accuracy of deforestation monitoring. The authors [42] have used the residual convolutional blocks for improved learning the forest changes using satellite images with early convergence as the network deepens. The proposed architecture is as shown in Figure 1.4.
Deep Q-network (DQN) for UAV pollution tracking
DQN, a deep reinforcement learning algorithm, has been used for UAV pollution tracking. The Deep Q-network-based solution efficiently identifies unhealthy polluted areas, contributing to effective pollution monitoring. The authors [10] have effectively used a Deep-Q network to get the rewards from the environment and based on which the air quality and the unhealthy areas were recognised. The proposed architecture by the authors is as given in Figure 1.5.
Vision Transformer (ViT) and diffusion models for environmental feature identification
Recent advancements include the use of Vision Transformer and Diffusion models for identifying and classifying environmental features. These architectures show promise in enhancing the efficiency of environmental monitoring and preservation. One such architecture was developed by the authors [43] for agriculture monitoring as shown in Figure 1.6.
F-RCNN-based aircraft identification for air pollution source identification
The F-RCNN-based aircraft identification framework, utilizing convolutional neural networks, accurately detects airplanes in satellite images. This has been applied to improve air pollution source identification in smart cities. One such research by the authors [44] has used an F-RCNN model for ozone depletion identification in stratosphere. The proposed architecture is given in Figure 1.7.
Ensemble approaches for multi-source transfer learning
Ensemble approaches, combining knowledge learned from multiple source stations through transfer learning, have been proposed to enhance air quality prediction. This helps overcome data shortage issues and improves the robustness of models. The authors [45] have proposed an ensemble model for energy prediction, and the proposed architecture is as given in Figure 1.8.
AI-SIoT for enhanced integration in environmental monitoring systems
The deep integration of AI in environmental monitoring systems, particularly through the use of AI-SIoT (Artificial Intelligence in the Semantic Internet of Things), has been explored. This integration significantly improves the accuracy, performance, efficiency, and reliability of monitoring systems. One such architecture by the authors [46] is given in Figure 1.9.
These architectures demonstrate the diverse applications of AI in environmental monitoring, showcasing their effectiveness in handling specific challenges within different domains of environmental science. The choice of architecture often depends on the nature of the data, the specific environmental parameter being monitored, and the desired outcomes of the monitoring system.
Table 1.1 Common environmental monitoring methodologies.
Methodology
Description
Application Areas
Advantages
Challenges and limitations
Empirical Modeling
[27]
Relies on observed data to develop relationships
Air quality modeling, hydrological modeling
Simple, data-driven, applicable with limited data
Limited generalization, may not capture dynamics
Analytical Modeling
[28]
Involves mathematical equations to represent systems
Groundwater flow modeling, climate modeling
Provides theoretical insights, computationally efficient
Limited to simplified systems, may lack realism
Numerical Modeling
[29]
Uses numerical simulations to solve complex equations
Weather forecasting, ocean circulation modeling
Captures complex interactions, spatial and temporal detail
Computationally intensive, requires high resources
Statistical Modeling
[30]
Applies statistical methods to analyze relationships
Species distribution modeling, trend analysis
Can handle uncertainties, useful for pattern recognition
Assumes statistical relationships, may oversimplify
Agent-Based Modeling (ABM)
[31]
Represents individual agents and their interactions
Ecological systems, land-use change modeling
Captures emergent behaviors, suitable for complex systems
Data-intensive, calibration challenges
Machine Learning (ML)
[32]
Utilizes algorithms to learn patterns from data
Air quality prediction, biodiversity assessment
Adaptable to various data types, automated pattern recognition
Black-box nature, potential for bias and overfitting
Geospatial Modeling
[33]
Integrates spatial data to model geographic phenomena
Land cover change modeling, urban planning
Incorporates spatial relationships, useful for spatial analysis
Relies on accurate spatial data, complexity in data integration
Hybrid Modeling
[34]
Combines multiple modeling approaches for synergy
Integrated water resource management, ecosystem modeling
Leverages strengths of different models, enhances accuracy
Complexity in model integration, increased computational burden
Remote Sensing
[35]
Uses satellite or aerial imagery for Earth observation
Land cover mapping, deforestation monitoring
Provides real-time data, wide coverage
Limited temporal resolution, dependency on weather conditions
System Dynamics Modeling
[36]
Represents dynamic feedback loops within a system
Urban growth modeling, ecological systems
Captures system behaviors over time, useful for policy analysis
Requires detailed understanding of system dynamics
Life Cycle Assessment (LCA)
[37]
Evaluates environmental impacts of products or systems
Product design, waste management
Considers entire life cycle, supports sustainable decision-making
Relies on accurate input data, limited for complex systems
IoT-based Sensor Framework
[38]
Sensor-based environment sensing for precision agriculture
Agriculture, Land Modeling
Captures the prime parameters of the environment under study
Relies on the sensor performance and data capture clarity
Figure 1.1 Convolution and LSTM model for environmental monitoring.
Figure 1.2 CNN model for vegetation monitoring.
Figure 1.3 Machine learning in environment modeling and prediction.
Figure 1.4 CNN model for forest change study.
Figure 1.5 DQN Architecture for environment modelling.
Figure 1.6 Transformer model for agricultural land monitoring.
Figure 1.7 F-RCNN model for environment modeling.
Figure 1.8 Ensemble learning for energy prediction.
Figure 1.9 Semantic IoT architecture.
1.6 Applications of AI in Environmental Monitoring
Recent advancements in AI, such as Vision Transformer and Diffusion models, have shown promise in environmental monitoring and preservation, particularly in identifying and classifying environmental features [47]. Artificial Neural Networks have been successfully applied in environmental quality monitoring, with the implementation of MATLAB leading to improved accuracy in evaluation results [48]. AI technologies have also been used to enhance efficiency in environmental monitoring and asset management, including the observation of bird behavior and condition assessment of concrete defects [49]. The deep integration of AI in environmental monitoring systems, particularly through the use of AI-SIoT, has been shown to significantly improve accuracy, performance, efficiency, and reliability [50]. A few examples of successful applications of AI in environmental monitoring, showcasing real-world implementations and their outcomes are given as follows.
IBM’s green horizon project
