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Rainfall Estimation Using Machine Learning Approaches with Raingauge, Radar, and Satellite Data

Rainfall is an essential variable in studying weather and has a vital role in the hydrological cycle. Therefore, accurate rainfall information becomes an urgent need. Machine learning approaches have also shown remarkable developments in various areas of life including rainfall estimation. This research combined rain gauge, radar and satellite data with several algorithms such as Decision Tree, Random Forest, Adaptive Boosting, and Gradient Boosting. Interestingly, it was found that the Gradient Boosting learning ensemble algorithm is best applied in rainfall estimation, with a correlation value (R2) is 0.740 and RMSE value is 1,765. On the other hand, weather radar reflectivity data significantly influences the integration of the data built into the model, suggesting that details of optimizing data are significantly required.