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Abstract

The Climate is becoming increasingly unpredictable, while the incidence of extreme weather is on the rise — both are contributing to surging global demand for advanced flood forecasting and monitoring services. This paper introduces an AI-powered Flood Monitoring and Warning System (FMWS) through an IoT sensor network, scalable real-time data analytics, and Machine Learning (ML) models to enhance the accuracy of prediction, risk analysis, and early warning dissemination in the notified areas. Hydrological and meteorological data would be collected by an ultrasonic sensor, a radar sensor, and a pressure sensor interfacing via GSM/GPRS, Wi-Fi, LoRa, or satellite network links. Machine learning models such as Random Forest and Neural Networks are used for detecting flood patterns and have achieved a prediction accuracy of 98%, 95% precision, recall of 95%, and a false alarm ratio of 3%. Outlet works for intermediate elevations were tested with a synthetic flood series and historical hydrological data cross-referencing, and the system showed performance in different scenarios. Comparative tests against accommodating the other systems (Logistic Regression and ARIMA in the baseline) demonstrated effectiveness in prediction accuracy, response liveness, and dynamic adaptability. In addition, the system also sends SMS, email, or mobile app alerts during emergencies and includes graphical representations, historical data, and multi-channel dashboards with data from the Flood Management and Warning System (FMWS) for an intuitive experience. The integration of IoT, AI, and geospatial techniques into a single flood management system is novel. It can also save money by preventing delays in response to disasters, thereby increasing flood resilience. These results are derived from the FMWS, which, powered by AI, assists with governance and contemporary disaster risk management planning.

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