Abstract
Rainfall is one of the most destructive natural disasters and is extremely difficult to model. As a result, a numerical analysis model based on a hydraulic theory and a machine learning algorithm were combined to produce a classification-based real-time rainfall prediction for the ground truth model. Using a two- dimensional inundation model and the environmental protection agency's storm model, the rainfall dataset is built in advance for various rainfall scenarios. The rainfall depth data for each map grid are divided by year based on the average rainfall prediction based on ground truth. If the observed rainfall is entered, a model is built to predict the representative cumulative volume. Rainfall-accuracy based on the actual situation in the proposed ADA SVM, ADA NB, MLP, and J48 algorithm. Keywords: ADA SVM,ADA NB,MLP, AND J48 ALGORITHM
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