# A Data-Driven Approach for Accurate Rainfall Prediction

## TL;DR

This paper presents a data-driven machine learning approach that utilizes atmospheric parameters, especially PWV, to improve rainfall prediction accuracy and reduce false alarms.

## Contribution

It systematically analyzes multiple atmospheric features and identifies the most impactful ones for rainfall prediction, enhancing existing methods.

## Key findings

- Achieved 80.4% true detection rate
- Reduced false alarm rate to 20.3%
- Overall accuracy of 79.6%

## Abstract

In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a four-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.