Flood Prediction and Adaptive Farming Solutions Using IoT, Machine Learning, and Remote Sensing for Climate-Resilient Agriculture

Flood is one of the most disastrous natural disasters to the people of agriculture, people and infrastructure. Mitigation of these risks and promotion of climate resilient agriculture depends on accurate flood predictions and progressive farming strategies. This paper presents an integrated model consisting of Internet of Things (IoT) sensors, Machine Learning (ML) and Remote Sensing technologies to provide precise, real time flood prediction and adaptive farming options. Using advanced deep learning algorithms and real time sensor data to predict and risk assess floods in real time, the model leverages real time satellite based Synthetic Aperture Radar (SAR). Another function of the model is to offer adaptive farming solutions including optimized irrigation, flood resilient crop selection, and disaster recovery recommendations for regions impacted by the disaster. A hybrid AI technique assimilating the social media data, and blockchain for integrity of data is more effective to make decisions for the local authorities and farmers. This study seeks to fill the gaps in interdisciplinary collaboration, data scarcity and governance and provide a solution for flood management and sustainable agriculture as a whole.