TL;DR
The proposed SVR-based ensemble forecasting approach reduced RMSE and MAE by approximately 43% and improved agreement with observed precipitation, demonstrating the potential of machine learning as an effective post-processing approach for precipitation forecasting over Thailand.
This study investigated the spatiotemporal trends and hybrid machine learning prediction of precipitation in Nigeria’s humid rainforest climatic zone using 45 years of monthly rainfall data from Calabar, Yenagoa, Benin City, and Ikeja using Random Forest–Support Vector Regression and Convolutional Neural Network–Long S...
Analyzing the performance of several forecasting models applied to Standardized Precipitation Index time series for the 1967–2021 period revealed a clear scale-dependent pattern: absolute forecast accuracy increased with the SPI aggregation scale—alongside more pronounced temporal persistence—though this did not necess...
Accurate weather prediction is essential for agriculture, aviation, transportation and disaster management, yet conventional statistical and physics-based forecasting models struggle to capture the non-linear and highly correlated relationships that exist among meteorological variables such as temperature, humidity, wi...
Weather prediction is crucial in agriculture, transportation, and disaster management. Accurate and timely forecasts are essential for making informed decisions and minimizing risks. This study proposes a hybrid machine learning approach for weather prediction, combining Ridge Regression for feature extraction and sele...
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