Analyzing the Impact of Meteorological Parameters on Rainfall Prediction

TL;DR

This paper investigates how different meteorological parameters influence rainfall prediction by analyzing five years of weather data, identifying key features through correlation analysis and machine learning techniques.

Contribution

It introduces a systematic approach to analyze meteorological features and employs feature selection to determine the most influential parameters for rainfall prediction.

Findings

Identified key meteorological features affecting rainfall

Analyzed correlations among weather parameters

Used machine learning for feature importance

Abstract

Rainfall is a climatic factor that affects many human activities like agriculture, construction, and forestry. Rainfall is dependent on various meteorological features and its prediction is a very complex task due to the dynamic climatic nature. A detailed study of different climatic features associated with the occurrence of rainfall should be made in order to understand the influence of each parameter in the context of rainfall. In this paper, we propose a methodical approach to analyze the affect of various parameters on rainfall. Our study uses $5$ years of meteorological data from a weather station located in the United States. The correlation and interdependence among the collected meteorological features were obtained. Additionally, we identified the most important meteorological features for rainfall prediction using a machine learning-based feature selection technique.