Coastal Flood Prediction Using Machine Learning

Abstract

Coastal flooding is one of the most severe natural hazards, causing significant damage to human life, infrastructure, and ecosystems in coastal regions. Accurate and timely flood prediction is essential for effective disaster preparedness and mitigation. This research presents a Coastal Flood Prediction System based on Machine Learning techniques to improve the accuracy and efficiency of flood forecasting. The proposed system utilizes environmental parameters such as rainfall, humidity, sea level, and wind speed to predict the likelihood of flood occurrence. Data preprocessing techniques are applied to clean and prepare the dataset, followed by the implementation of machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest. Among these, the Random Forest algorithm demonstrates superior performance in terms of prediction accuracy and reliability. The developed system is integrated into a user-friendly web application using Python and Flask, enabling users to obtain real-time flood predictions. Experimental results indicate that the proposed model effectively identifies flood-prone conditions and supports early warning decision-making. The study highlights the potential of machine learning in disaster management and provides a scalable framework for future flood prediction systems. The proposed approach can contribute to reducing the impact of floods by enabling proactive planning and timely response measures