A MACHINE LEARNING APPROACH FOR RAINFALL ESTIMATION

Abstract

Rainfall prediction is one of the challenging and uncertain tasks which has a significant
impact on human society. Timely and accurate predictions can help to proactively reduce
human and financial loss. This study presents a set of experiments which involve the use
of prevalent machine learning techniques to build models to predict whether it is going to
rain tomorrow or not based on weather data for that particular day in major cities of
Australia. This comparative study is conducted concentrating on three aspects: modeling
inputs, modeling methods, and pre-processing techniques. The results provide a
comparison of various evaluation metrics of these machine learning techniques and their
reliability to predict the rainfall by analyzing the weather data.