Satellite Image Based Water Bodies Assessment And Drought Prediction Using MachineLearning

Accurate drought forecasting plays a crucial role in ensuring sustainable water resource management and enhancing disaster readiness. This research focuses on harnessing satellite imagery and machine learning to improve the prediction of drought conditions. By tapping into the vast spatial and temporal insights provided by satellite platforms like Landsat-8, we aim to build predictive models capable of identifying drought onset early and assessing its severity. Critical parameters such as land surface temperature, vegetation vitality, and rainfall distribution are extracted from satellite datasets to serve as key input features. Through the application of advanced machine learning techniques, particularly the Random Forest algorithm, our study captures the complex, non-linear patterns embedded within the data, resulting in more reliable drought forecasts. The effectiveness of the proposed model is assessed using comprehensive validation methods, emphasizing key regression evaluation metrics. The results underscore the promise of blending remote sensing technologies with artificial intelligence to develop sophisticated drought prediction tools, ultimately supporting proactive mitigation and resilience strategies. This interdisciplinary methodology offers valuable contributions to the fields of environmental monitoring and disaster management.