Development of Hybrid Machine Learning Models for RealTime Drought and Flood Prediction in Agricultural Zones Using Multisource Environmental Data

Abstract

Droughts and storms are happening more often and getting worse, which is putting food security, farming output, and social and economic order in danger all over the world.  Predicting these unusual water events accurately and on time is very important for managing resources and preventing disasters in farming areas. This study shows how to make mixed machine learning models that can predict droughts and floods in real time using data from multiple natural sources, such as weather, soil wetness, satellite images, and water sensors.  The hybrid model takes advantage of the best features of both designs to improve the accuracy and timeliness of predictions. It does this by combining Convolutional Neural Networks (CNN) for extracting spatial features with Long Short-Term Memory (LSTM) networks for collecting temporal relationships. The model is learnt and tested on a large sample that comes from a variety of farming areas with different climates and landscapes.  To make the model more stable, data cleaning methods like normalisation, missing value imputation, and feature selection via Recursive Feature Elimination (RFE) are used.  We compare the mixed model's performance to standard models, such as CNN, LSTM, and traditional statistical methods. We use accuracy, precision, recall, and F1-score as key evaluation measures.  With a total accuracy of over 76%, precision of 77%, memory of 76%, and an F1-score of about 76.8%, the results show that the suggested hybrid method is better at catching the complex spatiotemporal patterns of drought and flood events.  The model's ability to make predictions in real time makes early warning systems and decision support tools possible for farmers, lawmakers, and water resource managers.