Application of Multi Linear Regression Model for Predicting Heavy Rainfall

Heavy rainfall prediction is crucial for various applications such as flood forecasting, water resource management, and agriculture. In this study, we propose a multi linear regression approach for predicting heavy rainfall events in a specific region. The method utilizes historical weather data including precipitation, temperature, humidity, wind speed, and atmospheric pressure as input features. By employing multi linear regression, we aim establishing a relationship between these meteorological Variables and occurrence of heavy rainfall. The model is trained on a dataset containing past weather observations and corresponding heavy rainfall occurrences. Evaluation of the model's performance is conducted Using measures such as mean squared error, R-squared, and root mean squared error.. Additionally, we compare the proposed multi linear regression approach with other machine learning techniques to assess its effectiveness in heavy rainfall prediction. The results demonstrate the potential of multi linear regression as a reliable method for forecasting heavy rainfall events, contributing to improved preparedness and mitigation strategies for adverse weather conditions