Flood AI: Data-Driven Prediction with C4.5 Decision Trees

Abstract

Flooding is one of the most severe natural disasters caused when heavy rain and swollen rivers destroy lives, homes, and the surrounding environment. It is also a major source of economic loss worldwide. The ability to predict floods in advance and respond on time plays a vital role in reducing the damage they cause. However, traditional forecasting methods often rely on complicated models and large resources, which are not always practical or accessible in vulnerable regions. In recent years, data-driven approaches such as machine learning have shown promising results in improving flood detection and prediction. This study explores the use of the C4.5 decision tree algorithm to predict floods in three regions of India. By analyzing environmental data such as rainfall, temperature, humidity, and river water levels, the model was able to make accurate predictions about flooding events. The findings show that data-based solutions can not only support existing forecasting systems but also provide quicker responses and better preparedness for communities at risk.