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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Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Paperback. Etat : 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. N° de réf. du vendeur 9786209699740
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Vendeur : California Books, Miami, FL, Etats-Unis
Etat : New. N° de réf. du vendeur I-9786209699740
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Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9786209699740
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9786209699740
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : 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. N° de réf. du vendeur 9786209699740
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Vendeur : Books Puddle, New York, NY, Etats-Unis
Etat : New. N° de réf. du vendeur 26405800531
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand. N° de réf. du vendeur 407353740
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Etat : New. PRINT ON DEMAND. N° de réf. du vendeur 18405800537
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Vendeur : CitiRetail, Stevenage, Royaume-Uni
Paperback. Etat : 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. N° de réf. du vendeur 9786209699740
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : 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. N° de réf. du vendeur 9786209699740
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