Abstract Rainfall forecasting is a vital factor in agricultural planning, water management, and disaster management, especially in a monsoon-reliant economy such as India, where the economy and livelihood of millions of people are inextricably connected to the conduct of the monsoons. It has been a significant challenge to forecast rainfall with great accuracy because of the complex, non-linear, and dynamic nature of climatic systems. The easy statistical techniques and single Machine Learning (ML) algorithms are incapable of understanding such compound patterns and make false predictions. The gap is filled in this study with the use of advanced ensemble learning methods, where hyperparameter tuning methods are used to enhance rainfall prediction in India. Sophisticated statistical and advanced ML models are used in predicting rainfall in India. It compared several regression techniques, such as Bayesian Regression (BR), K-Nearest Neighbors (KNN) Regressor, Random Forest (RF) Regressor, Elastic Net Regression (ENR), and Quantile Regression (QR), and new stacking combination models optimized by Random Grid Search (RGS), and Cross-Validation techniques. Those stacking ensemble models (SEM) go beyond traditional models by producing very small error values, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE), and optimal R-Squared values. The deep understanding of the seasonal and geographical variations of rainfall is under investigation to support the agriculture sector, water resources, and climate change mitigation. The project bridged a major existing gap in the literature because it combined ensemble approaches with hyperparameter optimization by demonstrating that these types of models could capture the complexity in rainfall data. The end of this study is an effective benchmark of safe, precise, and trustworthy systems of rainfall forecasting. Research Highlights <UnorderedList Mark="Bullet"> <ItemContent> Design a stacking combination models optimized by Random Grid Search (RGS) to enhance the prediction of rainfall in India. </ItemContent> <ItemContent> It compared several regression techniques, such as BR, KNN Regressor, RF Regressor, ENR, and QR with proposed model. </ItemContent> <ItemContent> The deep understanding of the seasonal and geographical variations of rainfall is under investigation to support the agriculture sector, water resources, and climate change mitigation. </ItemContent> <ItemContent> The project combined ensemble approaches with hyperparameter optimization by demonstrating that these types of models could capture the complexity in rainfall data. </ItemContent> </UnorderedList>

Stacking ensemble model for rainfall prediction using hyperparameter optimization

期刊论文
发表日期：

2026年5月27日

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Ankita Harshad Tidake,
S K Yadav,
Vivek S Deshpande,
Priyanka Waghmare,
Sweta Wankhade,
Sanjay Mate

摘要

Abstract

Rainfall forecasting is a vital factor in agricultural planning, water management, and disaster management, especially in a monsoon-reliant economy such as India, where the economy and livelihood of millions of people are inextricably connected to the conduct of the monsoons. It has been a significant challenge to forecast rainfall with great accuracy because of the complex, non-linear, and dynamic nature of climatic systems. The easy statistical techniques and single Machine Learning (ML) algorithms are incapable of understanding such compound patterns and make false predictions. The gap is filled in this study with the use of advanced ensemble learning methods, where hyperparameter tuning methods are used to enhance rainfall prediction in India. Sophisticated statistical and advanced ML models are used in predicting rainfall in India. It compared several regression techniques, such as Bayesian Regression (BR), K-Nearest Neighbors (KNN) Regressor, Random Forest (RF) Regressor, Elastic Net Regression (ENR), and Quantile Regression (QR), and new stacking combination models optimized by Random Grid Search (RGS), and Cross-Validation techniques. Those stacking ensemble models (SEM) go beyond traditional models by producing very small error values, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE), and optimal R-Squared values. The deep understanding of the seasonal and geographical variations of rainfall is under investigation to support the agriculture sector, water resources, and climate change mitigation. The project bridged a major existing gap in the literature because it combined ensemble approaches with hyperparameter optimization by demonstrating that these types of models could capture the complexity in rainfall data. The end of this study is an effective benchmark of safe, precise, and trustworthy systems of rainfall forecasting.

Research Highlights

Design a stacking combination models optimized by Random Grid Search (RGS) to enhance the prediction of rainfall in India.

It compared several regression techniques, such as BR, KNN Regressor, RF Regressor, ENR, and QR with proposed model.

The deep understanding of the seasonal and geographical variations of rainfall is under investigation to support the agriculture sector, water resources, and climate change mitigation.

The project combined ensemble approaches with hyperparameter optimization by demonstrating that these types of models could capture the complexity in rainfall data.