Optimal Solution For Rainfall Prediction

Abstract

Rainfall prediction is a critical challenge with farreaching
implications for agriculture, disaster
management, and climate research. This research
presents a machine learning-based system designed
to forecast rainfall using historical weather data,
incorporating factors such as temperature, humidity,
wind speed, and atmospheric pressure. The system
employs models like Decision Trees and Random
Forests to analyze past weather patterns and identify
key predictors of rainfall. Advanced techniques in
data preprocessing and feature selection are utilized
to optimize the accuracy of predictions. The
framework offers actionable insights for
meteorologists, farmers, and policymakers by
providing precise and reliable rainfall forecasts. A
user-friendly interface delivers real-time predictions
and visualizations, aiding decision-making in
weather-dependent industries. It can enhance
preparedness, resource allocation, and disaster
mitigation efforts, contributing to improved climate
resilience and operational efficiency.