PRECIPITATION PREDICTION USING MACHINE LEARNING, DEEP LEARNING, AND TIME SERIES MODELS ACROSS HIGH PLAINS

Machine learning (ML) techniques are rapidly emerging as effective tools in predicting complex hydrological processes. This study aims to comparatively assess the efficacy of machine learning (ML), deep learning (DL), and time series modeling approaches for predicting precipitation patterns across High Plains. We intend to compare the performance of ML and DL methods against traditional time series techniques commonly used in meteorological predictions. The model performances will be evaluated based on their key performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE).