Development of Explainable AI (XAI) Based Model for Prediction of Heavy/High Impact Rain Events Using Satellite Data

Abstract

A significant development in meteorological science has been the creation of Explainable AI (XAI) models that use satellite data to predict heavy/high-impact rain events. There is a growing demand for predictive models that not only provide accurate forecasts but also provide insights into the underlying decision-making process due to the increasing frequency and severity of extreme weather patterns. It is difficult to use traditional machine learning (ML) models in high-stakes situations like weather forecasting due to their lack of transparency. XAI fills this void by improving the interpretability of models, which is essential for comprehending the factors that lead to extreme rainfall events. This paper looks at how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models can be used to combine satellite-based data with XAI models to predict heavy rain events. The spatiotemporal transformer framework, attention mechanisms, and capsule networks are some of the cutting-edge deep learning architectures that we are looking into for ways to boost prediction accuracy and dependability. The study also emphasizes how model explainability helps end-users like meteorologists, emergency response teams, and policymakers build trust. This study aims to provide a comprehensive framework for deploying satellite-driven rain prediction models in real-world applications by examining recent advancements in XAI for climate risk assessment. In addition, we address issues related to data quality, model interpretability, computational efficiency, and scalability when integrating these models with existing weather forecasting systems. The findings suggest that XAI models have a lot of potential to change weather forecasting practices by making it easier to be prepared for extreme rainfall events and providing clear insights into the decisions made by the models. This paper paves the way for the widespread use of weather prediction tools that are more reliable, actionable, and interpretable in disaster management and climate adaptation strategies.