Affiliation:
1. 1 Department of Computer Science, King Fahad Naval Academy, Al Jubail 35512, Saudi Arabia
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.
Reference58 articles.
1. An optimized model using LSTM network for demand forecasting
2. Forecasting of medium-term rainfall using artificial neural networks: case studies from eastern Australia;Abbot,2018
3. Forecasting and modeling on average rainwater and vapor pressure in Chelyabinsk Russia using deep learning models;Abotaleb;In,2022
4. Short-term precipitation forecast based on the PERSIANN system and LSTM recurrent neural networks;Akbari Asanjan;Journal of Geophysical Research: Atmospheres,2018
5. Forecasting COVID-19 infection using encoder–decoder LSTM and attention LSTM algorithms;Alakkari;Journal of Intelligent Systems & Internet of Things,2023
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献