Comparative Analysis of Flood Prediction Using Linear Regression

Flood prediction as we known is a important role in reducing the impacts of effective disasters . This paper says that a linear regression-based model is designed for forecasting flood occurrences by analyzing historical hydrological data. Dataset that includes rainfall patterns, river discharge levels, and soil moisture content, it is proposed with high accuracy. The approach predicts flood risks evaluation incorporates some metrics such as mean squared error, R- squared, and prediction accuracy to validate model effectiveness. ANN, SMV, DT, and other advanced ML methods are contrasted with each other. Experimental result reveal that while linear regression performs efficiently in terms of simplicity and interpretability, its accuracy can be enhanced by interating feature selection technologies. In the conclusion, the linear regression approach outlined in this study demonstrates substantial potential for flood prediction applications, gives awareness for early warning systems and informed disaster response planning. Keywords- Flood prediction, linear regression, hydrological modeling, Disaster management, Machine learning.