Floods and Landslide Prediction Using Machine Learning

Abstract

Natural disasters such as floods and landslides cause a large number of deaths and destruction of property and infrastructure every year, especially in regions that are prone to such disasters. Early prediction of natural disasters is very important for their effective management. Conventional methods of prediction involve the use of physical models and manual processing, which are often prone to inaccuracies owing to the complexities involved in environmental interactions and the rapidly changing climate patterns. To overcome these difficulties, this project proposes the use of a machine learning technique for predicting floods and landslides. The proposed system takes into account past and current data related to rainfall intensity, water levels in rivers, soil moisture content, slope, land use, temperature, and geological conditions.

Different machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression are trained and tested to identify patterns and relationships associated with floods and landslides. Techniques for data preprocessing, such as normalization, feature selection, and handling missing data, are also implemented.