Disaster Prediction Using Artificial Intelligence
Editat de Disha Thakur, Chander Prakash, Sanjay Kumar, Himanshu Sharmaen Limba Engleză Hardback – 9 noi 2026
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Notă biografică
Disha Thakur, PhD, is an Assistant Professor of Civil Engineering at the University Institute of Technology. She has published 16 research papers and book chapters in reputed international journals and conference proceedings. Her research interests include geotechnical engineering and solid waste management.
Chander Prakash, PhD, is an Associate Professor in the Department of Civil Engineering at the National Institute of Technology, Hamirpur, India. He has published more than 25 research papers in reputed national and international journals and conference proceedings. His research interests include the application of remotely sensed satellite imagery and GIS in natural resource management, hazard analysis, and the planning and management of development projects.
Sanjay Kumar, PhD, is an Assistant Professor at the University Institute of Technology at Himachal Pradesh University. He has published 24 research papers in reputed national and international journals and 18 research papers in international conference proceedings. His research areas include power systems, renewable energy, network planning, microgrids, optimization, GIS, and machine learning.
Himanshu Sharma, PhD, is an Associate Professor at G.H. Raisoni College of Engineering and Management, Nagpur. He has published more than ten research papers in refereed journals and conference proceedings. His areas of expertise include power electronics, machine learning, and optimization techniques.
Chander Prakash, PhD, is an Associate Professor in the Department of Civil Engineering at the National Institute of Technology, Hamirpur, India. He has published more than 25 research papers in reputed national and international journals and conference proceedings. His research interests include the application of remotely sensed satellite imagery and GIS in natural resource management, hazard analysis, and the planning and management of development projects.
Sanjay Kumar, PhD, is an Assistant Professor at the University Institute of Technology at Himachal Pradesh University. He has published 24 research papers in reputed national and international journals and 18 research papers in international conference proceedings. His research areas include power systems, renewable energy, network planning, microgrids, optimization, GIS, and machine learning.
Himanshu Sharma, PhD, is an Associate Professor at G.H. Raisoni College of Engineering and Management, Nagpur. He has published more than ten research papers in refereed journals and conference proceedings. His areas of expertise include power electronics, machine learning, and optimization techniques.
Cuprins
Preface xv
1 Introduction to AI: Applications in Disaster Prediction 1
Shivi Sharma and D. D. Sharma
1.1 Introduction 2
1.2 Intelligent System Components 4
1.3 Emerging Trends in Artificial Intelligence 8
1.4 AI Algorithms and Their Applications 10
1.5 Advantages of Intelligent Systems 14
1.6 Disadvantages and Challenges of Intelligent Systems 18
1.7 Summary and Conclusion 21
2 Machine Learning-Based Wildfire Susceptibility Assessment in Southern California 27
Divyansh Sharma and Disha Thakur
2.1 Introduction 28
2.2 Study Area 29
2.3 Methodology 30
2.4 Results and Discussion 36
2.5 Limitations and Future Scope of Work 40
2.6 Conclusion 41
3 Enhancing Disaster Resilience in the Himalayan Region through AI and Emerging Technologies 47
Neelam Sidhu, Chandni Thakur, Sahil Sharma, Kanwarpreet Singh and Abhishek Sharma
3.1 Introduction 48
3.2 Methodology 50
3.3 Thematic Synthesis of GeoAI Application in Disaster Resilience 52
3.4 Sociocultural and Geopolitical Implications of AI-Based Early Warning Systems in the Himalayan Region 57
3.5 Future Directions for Disaster Management in the IHR 60
3.6 Conclusion 63
4 Long-Term Climate-Vegetation Interactions: Assessing the Influence of Rainfall and Temperature on NDVI in Kullu District, Himachal Pradesh, India (1982-2024) 67
Vanshika Bhardwaj, Har Amrit Singh Sandhu, Shubham Bhadoriya and Prakul Lath
4.1 Introduction 68
4.2 Study Area 70
4.3 Data Used 70
4.4 Methodology 72
4.5 Results and Discussion 73viii Contents
4.6 Conclusion 81
5 AI-Driven Early Warning and Automated Traffic Control for Infrastructure Resilience During Extreme Events 85
Akshat Sharma, Richa Sharma and Amardeep Boora
5.1 Introduction 86
5.2 Literature Review 88
5.3 System Architecture 90
5.4 Implementation 104
5.5 Mountain-Valley Cloudburst Scenario 112
5.6 Conclusion 115
6 ANN-Based Prediction of Road Accident Black Spots 121
Har Amarit Singh Sandhu, Disha Thakur, Sanjay Kumar, Vedant Singh and Abarar Ahamad
6.1 Introduction 122
6.2 Study Area 125
6.3 Research Methodology 127
6.4 Results and Discussions 132
6.5 Policy Suggestions and Remedial Measures 135
6.6 Conclusions 137
7 Data-Driven AI Model for Predicting Infrastructure Damage from Earthquakes 141
Swati Singh, Anjali Sharma, Sanjay Kumar, Ankush Tanta and Isha Gupta
7.1 Introduction 142
7.2 Literature Survey 148
7.3 Data and Methods 152
7.4 Key Results and Discussion 158
7.5 Conclusion 164
8 Hyperspectral Imaging and Deep Learning for Disaster Monitoring Systems 169
P. Shanmuga Sundari, J. Karthikeyan, Sunil Kumar Palaniyappan and R. Vishnu Priya
8.1 Introduction 170
8.2 Literature Review 170
8.3 Proposed Methodology 171
8.4 Experimental Setup 177
8.5 Conclusion 179
9 Machine Learning-Based Cloudburst Prediction for Climate Resilience in Himachal Pradesh 183
Talari Ganesh
9.1 Introduction 184
9.2 Literature Review 184
9.3 Methodology 185
9.4 Results and Discussion 190
9.5 Conclusion 199Contents xi
10 Role of AI and Blockchain Technology in Disaster Prediction and Its Management 203
Supriya Krishnan and Aditya Bharti
10.1 Introduction 204
10.2 AI and Its Role in Forecasting Disaster and Its Management 206
10.3 Blockchain Technology (BT) and Its Role in Forecasting Disaster and Its Management 221
10.4 Combining AI and Blockchain Technology for Disaster Management 229
10.5 Benefits of Synergizing AI and BT for Disaster Management 233
10.6 Real-World Applications of Synergistic Application of AI and Blockchain Technology in Disaster Management 236
10.7 Conclusions 241
11 Predictive Digital Twin for Disaster Prevention and Management in Smart City 249
Sandhya Avasthi and Suman Lata Tripathi
11.1 Introduction 250
11.2 Predictive Digital Twin Architecture 254
11.3 Predictive Digital Twin Applications for Smart City 255
11.4 Predictive Digital Twin Framework for Smart City 259
11.5 Integration of Data and Predictive Modeling 261
11.6 Challenges in Predictive Digital Twin 264
11.7 Conclusion 267
12 A Comprehensive Review on Statistical and Artificial Intelligence Techniques for Landslide Analysis 271
Amol Sharma, Chander Prakash and Siddharth Garia
12.1 Introduction 272
12.2 Landslide Mechanisms 273
12.3 Methodology of the Review 278
12.4 Conclusion 292
13 Integrating AI, IoT, and Drones for Smart Natural Disaster Monitoring and Response 307
Vinay Anand and Himanshu Sharma
13.1 Introduction 307
13.2 Methodology and Framework Design 313
13.3 Literature Review 314
13.4 Results, Discussion, and Comparative Analysis 315
13.5 Challenges, Limitations, and Future Research Directions 318
13.6 Conclusion 319
14 Prediction to Resilience: Next-Generation Artificial Intelligence for Disaster Risk Management 325
Abhishek Sharma, Kanwarpreet Singh, Ajay Dheekwal and Neelam Sidhu
14.1 Introduction 326
14.2 Data Foundation for AI in Disaster Prediction and Management 328
14.3 Generative Models for Disaster Prediction and Management 331
14.4 Data Foundations for AI-Driven Disaster Prediction and Management 334
14.5 Generative Models for Enhanced Disaster Prediction and Scenario Simulation 336
14.6 Advancements in Natural Language Processing and Large Language Models for Disaster Intelligence 337
14.7 Autonomous Systems and Robotics Response and Management 339
14.8 Quantum Computing in Disaster Prediction and Management 339
14.9 Integrated AI System for Holistic Disaster Prediction and Management 340
14.10 Societal, Ethical, and Governance Consideration in AI-Driven Disaster Management 342
14.11 Conclusions 343
References 345
Index 351
1 Introduction to AI: Applications in Disaster Prediction 1
Shivi Sharma and D. D. Sharma
1.1 Introduction 2
1.2 Intelligent System Components 4
1.3 Emerging Trends in Artificial Intelligence 8
1.4 AI Algorithms and Their Applications 10
1.5 Advantages of Intelligent Systems 14
1.6 Disadvantages and Challenges of Intelligent Systems 18
1.7 Summary and Conclusion 21
2 Machine Learning-Based Wildfire Susceptibility Assessment in Southern California 27
Divyansh Sharma and Disha Thakur
2.1 Introduction 28
2.2 Study Area 29
2.3 Methodology 30
2.4 Results and Discussion 36
2.5 Limitations and Future Scope of Work 40
2.6 Conclusion 41
3 Enhancing Disaster Resilience in the Himalayan Region through AI and Emerging Technologies 47
Neelam Sidhu, Chandni Thakur, Sahil Sharma, Kanwarpreet Singh and Abhishek Sharma
3.1 Introduction 48
3.2 Methodology 50
3.3 Thematic Synthesis of GeoAI Application in Disaster Resilience 52
3.4 Sociocultural and Geopolitical Implications of AI-Based Early Warning Systems in the Himalayan Region 57
3.5 Future Directions for Disaster Management in the IHR 60
3.6 Conclusion 63
4 Long-Term Climate-Vegetation Interactions: Assessing the Influence of Rainfall and Temperature on NDVI in Kullu District, Himachal Pradesh, India (1982-2024) 67
Vanshika Bhardwaj, Har Amrit Singh Sandhu, Shubham Bhadoriya and Prakul Lath
4.1 Introduction 68
4.2 Study Area 70
4.3 Data Used 70
4.4 Methodology 72
4.5 Results and Discussion 73viii Contents
4.6 Conclusion 81
5 AI-Driven Early Warning and Automated Traffic Control for Infrastructure Resilience During Extreme Events 85
Akshat Sharma, Richa Sharma and Amardeep Boora
5.1 Introduction 86
5.2 Literature Review 88
5.3 System Architecture 90
5.4 Implementation 104
5.5 Mountain-Valley Cloudburst Scenario 112
5.6 Conclusion 115
6 ANN-Based Prediction of Road Accident Black Spots 121
Har Amarit Singh Sandhu, Disha Thakur, Sanjay Kumar, Vedant Singh and Abarar Ahamad
6.1 Introduction 122
6.2 Study Area 125
6.3 Research Methodology 127
6.4 Results and Discussions 132
6.5 Policy Suggestions and Remedial Measures 135
6.6 Conclusions 137
7 Data-Driven AI Model for Predicting Infrastructure Damage from Earthquakes 141
Swati Singh, Anjali Sharma, Sanjay Kumar, Ankush Tanta and Isha Gupta
7.1 Introduction 142
7.2 Literature Survey 148
7.3 Data and Methods 152
7.4 Key Results and Discussion 158
7.5 Conclusion 164
8 Hyperspectral Imaging and Deep Learning for Disaster Monitoring Systems 169
P. Shanmuga Sundari, J. Karthikeyan, Sunil Kumar Palaniyappan and R. Vishnu Priya
8.1 Introduction 170
8.2 Literature Review 170
8.3 Proposed Methodology 171
8.4 Experimental Setup 177
8.5 Conclusion 179
9 Machine Learning-Based Cloudburst Prediction for Climate Resilience in Himachal Pradesh 183
Talari Ganesh
9.1 Introduction 184
9.2 Literature Review 184
9.3 Methodology 185
9.4 Results and Discussion 190
9.5 Conclusion 199Contents xi
10 Role of AI and Blockchain Technology in Disaster Prediction and Its Management 203
Supriya Krishnan and Aditya Bharti
10.1 Introduction 204
10.2 AI and Its Role in Forecasting Disaster and Its Management 206
10.3 Blockchain Technology (BT) and Its Role in Forecasting Disaster and Its Management 221
10.4 Combining AI and Blockchain Technology for Disaster Management 229
10.5 Benefits of Synergizing AI and BT for Disaster Management 233
10.6 Real-World Applications of Synergistic Application of AI and Blockchain Technology in Disaster Management 236
10.7 Conclusions 241
11 Predictive Digital Twin for Disaster Prevention and Management in Smart City 249
Sandhya Avasthi and Suman Lata Tripathi
11.1 Introduction 250
11.2 Predictive Digital Twin Architecture 254
11.3 Predictive Digital Twin Applications for Smart City 255
11.4 Predictive Digital Twin Framework for Smart City 259
11.5 Integration of Data and Predictive Modeling 261
11.6 Challenges in Predictive Digital Twin 264
11.7 Conclusion 267
12 A Comprehensive Review on Statistical and Artificial Intelligence Techniques for Landslide Analysis 271
Amol Sharma, Chander Prakash and Siddharth Garia
12.1 Introduction 272
12.2 Landslide Mechanisms 273
12.3 Methodology of the Review 278
12.4 Conclusion 292
13 Integrating AI, IoT, and Drones for Smart Natural Disaster Monitoring and Response 307
Vinay Anand and Himanshu Sharma
13.1 Introduction 307
13.2 Methodology and Framework Design 313
13.3 Literature Review 314
13.4 Results, Discussion, and Comparative Analysis 315
13.5 Challenges, Limitations, and Future Research Directions 318
13.6 Conclusion 319
14 Prediction to Resilience: Next-Generation Artificial Intelligence for Disaster Risk Management 325
Abhishek Sharma, Kanwarpreet Singh, Ajay Dheekwal and Neelam Sidhu
14.1 Introduction 326
14.2 Data Foundation for AI in Disaster Prediction and Management 328
14.3 Generative Models for Disaster Prediction and Management 331
14.4 Data Foundations for AI-Driven Disaster Prediction and Management 334
14.5 Generative Models for Enhanced Disaster Prediction and Scenario Simulation 336
14.6 Advancements in Natural Language Processing and Large Language Models for Disaster Intelligence 337
14.7 Autonomous Systems and Robotics Response and Management 339
14.8 Quantum Computing in Disaster Prediction and Management 339
14.9 Integrated AI System for Holistic Disaster Prediction and Management 340
14.10 Societal, Ethical, and Governance Consideration in AI-Driven Disaster Management 342
14.11 Conclusions 343
References 345
Index 351