(untitled) (Al-Aasar, 2025)
Flash floods and extreme rainfall events lead to disastrous outcomes all over the world, which stretch the very survival of human beings and their physical and human infrastructures, even their economies. Therefore, efficient disaster preparedness needs an impeccable forecasting. Most of the classical hydrological and meteorological models are often inadequate in such a way that they do not take into account nonlinear dependencies and temporal variations in rainfall, which in turn cause such flaws as error in their predictive outcomes. In this research, machine-learning-based time series analysis is adopted for improving flood forecasting with the Kerala Flood Dataset (1901-2018). Heavy rain trend prediction applies ARIMA and Long Short-Term Memory (LSTM) networks. The LSTM significantly outperformed ARIMA with an RMSE of 64.5 opposed to 87.2 for ARIMA, thereby attesting its worth for modeling long-term dependencies in addition to the sequential changes of rainfall. To classify for flash floods, three separate classifiers were used, with Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. The best accuracy was achieved by the Random Forest classifier: 96.1%, whereas KNN and Logistic Regression yielded 91.2 and 86.5%, respectively. It underscores the competence of ensemble learning in extreme-weather classification. It also did feature engineering, from rolling means to lags, which is going to improve model performance. The models’ performance has been found effective and appropriate through comparative analysis with respect to accuracy, precision, recall, and RMSE. Human real-world scenarios equipped the models to go further and have been deployed as API-based early warning systems, interfaced with IoT-driven real-time weather monitoring, and coupled with cloud computing for continuous updating and real-time flood risk appraisal. The results will give this study another validation and make a strong case for using AI-driven predictive analytics in disaster resilience and climate adaptation. It has been demonstrated that machine learning significantly improves the accuracy of early warning systems for flood prediction, thus contributing to disaster management and risk mitigation strategies, using LSTM in time series forecasting and Random Forest in flood classification. Future works will involve capturing real-time satellite data associated with hydrological parameters and the hybridization of deep learning approaches for future improvements in model predictions of extreme weather forecasting.