Flood disasters increasingly threaten rural and semi-urban communities due to climate variability and inadequate early warning systems. This book presents a distributed Edge-AI and IoT-driven cyber-physical architecture for predictive flood risk modeling and resilience governance. The framework integrates multi-parameter environmental sensing, hybrid AI forecasting using ARIMA and LSTM models, and resilient LoRa–GSM communication networks to enable short-term flood probability estimation. Unlike conventional threshold-based systems, it supports proactive risk classification, multi-channel alert dissemination, vulnerability-aware evacuation prioritization, and two-way SOS communication. Solar-powered sensing nodes ensure energy autonomy, while redundancy enhances reliability during extreme conditions. By combining predictive intelligence with community-centric coordination, the system advances scalable climate resilience for resource-constrained regions.
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Dr. Kirti Hemant Wanjale received her Ph.D degree from Faculty of Computer Engineering from SSSTUMS, Sehore MP. She is Currently Working as Professor, Department of Computer Engineering at Vishwakarma Institute of Technology Pune. She has 22 years of experience. Her main research interests are Wireless Sensor Networks, Internet of Things (IoT).
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Paperback. Zustand: new. Paperback. Flood disasters increasingly threaten rural and semi-urban communities due to climate variability and inadequate early warning systems. This book presents a distributed Edge-AI and IoT-driven cyber-physical architecture for predictive flood risk modeling and resilience governance. The framework integrates multi-parameter environmental sensing, hybrid AI forecasting using ARIMA and LSTM models, and resilient LoRa-GSM communication networks to enable short-term flood probability estimation. Unlike conventional threshold-based systems, it supports proactive risk classification, multi-channel alert dissemination, vulnerability-aware evacuation prioritization, and two-way SOS communication. Solar-powered sensing nodes ensure energy autonomy, while redundancy enhances reliability during extreme conditions. By combining predictive intelligence with community-centric coordination, the system advances scalable climate resilience for resource-constrained regions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9786209699740
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Flood disasters increasingly threaten rural and semi-urban communities due to climate variability and inadequate early warning systems. This book presents a distributed Edge-AI and IoT-driven cyber-physical architecture for predictive flood risk modeling and resilience governance. The framework integrates multi-parameter environmental sensing, hybrid AI forecasting using ARIMA and LSTM models, and resilient LoRa-GSM communication networks to enable short-term flood probability estimation. Unlike conventional threshold-based systems, it supports proactive risk classification, multi-channel alert dissemination, vulnerability-aware evacuation prioritization, and two-way SOS communication. Solar-powered sensing nodes ensure energy autonomy, while redundancy enhances reliability during extreme conditions. By combining predictive intelligence with community-centric coordination, the system advances scalable climate resilience for resource-constrained regions. 80 pp. Englisch. Bestandsnummer des Verkäufers 9786209699740
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Paperback. Zustand: new. Paperback. Flood disasters increasingly threaten rural and semi-urban communities due to climate variability and inadequate early warning systems. This book presents a distributed Edge-AI and IoT-driven cyber-physical architecture for predictive flood risk modeling and resilience governance. The framework integrates multi-parameter environmental sensing, hybrid AI forecasting using ARIMA and LSTM models, and resilient LoRa-GSM communication networks to enable short-term flood probability estimation. Unlike conventional threshold-based systems, it supports proactive risk classification, multi-channel alert dissemination, vulnerability-aware evacuation prioritization, and two-way SOS communication. Solar-powered sensing nodes ensure energy autonomy, while redundancy enhances reliability during extreme conditions. By combining predictive intelligence with community-centric coordination, the system advances scalable climate resilience for resource-constrained regions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786209699740
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Flood disasters increasingly threaten rural and semi-urban communities due to climate variability and inadequate early warning systems. This book presents a distributed Edge-AI and IoT-driven cyber-physical architecture for predictive flood risk modeling and resilience governance. The framework integrates multi-parameter environmental sensing, hybrid AI forecasting using ARIMA and LSTM models, and resilient LoRa-GSM communication networks to enable short-term flood probability estimation. Unlike conventional threshold-based systems, it supports proactive risk classification, multi-channel alert dissemination, vulnerability-aware evacuation prioritization, and two-way SOS communication. Solar-powered sensing nodes ensure energy autonomy, while redundancy enhances reliability during extreme conditions. By combining predictive intelligence with community-centric coordination, the system advances scalable climate resilience for resource-constrained regions.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. Bestandsnummer des Verkäufers 9786209699740
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