Supervised Rainfall Learning Model Using Machine Learning Algorithms

Abstract

Unpredictable and uncertain volume of the rainfall is the serious nature disaster. In current, available rainfall forecasting model predict rainfall volume hourly, weekly or monthly. This work proposed a supervised learning model which is based on machine leaning algorithms of data mining. This approach classify the low, mid and high volume of rainfall. Proposed approach is practically implemented on different uncertain heavy rainfall regions and compare the accuracy and measured the accuracy by ROC area of classifiers such as Random Forest, SMO, Naive Bayes and Multilayer Perceptron (MLP).