AI-driven improvement of monthly average rainfall forecasting in Mecca using grid search optimization for LSTM networks

Abstract

Abstract Predicting the average monthly rainfall in Mecca is crucial for sustainable development, resource management, and infrastructure protection in the region. This study aims to enhance the accuracy of long short-term memory (LSTM) deep regression models used for rainfall forecasting using an advanced grid search-based hyperparameter optimization technique. The proposed model was trained and validated on a historical dataset of Mecca's monthly average rainfall. The model's performance improved by 5.0% post-optimization, reducing the root-mean-squared error (RMSE) from 0.1201 to 0.114. The results signify the value of grid search optimization in improving the LSTM model's accuracy, demonstrating its superiority over other common hyperparameter optimization techniques. The insights derived from this research provide valuable input for decision-makers in effectively managing water resources, mitigating environmental risks, and fostering regional development.