Machine Learning Techniques to Predict Rainfall of Vidarbh Region

Abstract

Accurate daily rainfall forecasting is vital for proper agricultural planning and managing resources efficiently. This study evaluates the performance of various methods, with an emphasis on temperature and precipitation, such as linear regression, random forest, SVM regression, and XGBoost. Linear regression displays minimal RMSE and a flawless R-squared, whereas random forest performs effectively with low RMSE and encouraging R2 results. However, the presence of a negative R-squared indicates potential overfitting. The MAE, MSE, and RMSE statistics for SVM are competitive. The study draws attention to the unexplored Vidarbha region, which includes 11 districts, using Nagpur district as a representative instance. Additionally, future plans involve the utilization of deep learning models like ARIMA and LSTM to enhance rainfall prediction accuracy across Vidarbha. This investigation yields valuable insights into climate prediction, offering support for well-informed decision-making.