Abstract

Floods are among the most devastating natural disasters globally, resulting in significant loss of life, displacement, and economic disruption. Traditional flood forecasting models struggle with the complexities of dynamic environmental data and spatial-temporal dependencies. This paper presents a deep learning-based framework that integrates satellite imagery and Internet of Things (IoT) sensor data for improved flood forecasting accuracy. By leveraging Convolutional Neural Networks (CNNs) for image-based pattern recognition and Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, for temporal sequence prediction, the proposed model achieves high performance in forecasting flood events. Fusion techniques combining satellite and sensor data are applied to enhance situational awareness. Experimental evaluations using datasets from real flood-prone regions demonstrate the effectiveness of the approach in terms of accuracy, timeliness, and reliability.

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