Flood Prediction Using Machine Learning

Abstract

Flood prediction is of prime importance in management and mitigation of flood risks in flood-prone areas. In this project, a machine learning-based system has been proposed to predict floods on the basis of parameters like temperature, cloud cover, and humidity. From several fitted algorithms, such as Decision Tree, Random Forest, K-Nearest Neighbors, and XGBoost, the best predictive model was determined. XGBoost turned out to be the most precise, with an f1-score. In order to make this model practical, a Flask web application was created whereby users could input data and be given flood predictions easily. Indeed, the success of this system goes on to prove that through the use of machine learning, the basis of flood prediction can be highly improved to enable communities to adequately prepare for such an event. In order to improve this system, more historical data can be used with model parameter refinement. Thus, machine learning has a bright future in the sector of natural disaster management.