Intelligent Web App for Flash Flood Prediction in Nigeria’s Coastal Regions
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Abstract
Many coastal cities in Nigeria and around the world are faced with the menace of flash floods and many times, it temporarily disrupts the socio-economic activities of residents. This project aims to address this challenge by developing a smart web application using machine learning to intelligently predict flash flood occurrence and offer recommendations. In order to achieve this, the random forest machine learning algorithm is utilized to analyze environmental data such as rainfall, river levels, and soil moisture necessary for the prediction of a flood which are captured in real-time using the OpenMeteo API. The machine learning model is then trained using these environmental variables and integrated into a web application for easy user interaction. The frontend of the web application is built with TypeScript, React.js, and Tree.js, providing an interactive and user-friendly interface for visualizing flood predictions, while the backend is built using MongoDB and python (FLASK framework). The goal is to offer accurate, real-time flood forecasts to help individuals prepare and respond effectively. This project demonstrates the integration of data science and web development to create a practical tool for disaster risk management. The random forest model was evaluated using the standard metrics for evaluating machine learning models and showed the following results; Accuracy of 96%, precision of 75% and recall of 91%. In addition, the model, showed a Real-time latency of less that one second, which is indicative of a fast response to changing environmental data input. Since flood conditions can change rapidly, this low real-time latency shows that the web is able to respond quickly to new sensor or satellite data input.
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