FLOOD PREDICTION USING SUPPORT VECTOR MACHINE ALGORITHM ON MOBILE APPLICATION

Abstract

Floods are recurrent natural disasters that can have a devastating impact on societies, economies, and the environment. Therefore, it is critical to predict and manage flood situations promptly to minimize the damage they cause. However, many people are unaware of flood risks, and there are limited mobile applications that can provide timely and accurate flood predictions. This study explores the application of the Support Vector Machine (SVM) algorithm for flood prediction in a mobile application. The aim is to provide users with timely and accurate flood predictions, enabling them to make informed decisions and take necessary precautions to mitigate flood impacts. By integrating the SVM algorithm into a mobile application, users gain convenient access to flood predictions, empowering them to be better prepared for potential flooding events. The user-friendly platform delivers critical flood forecasts, ensuring individuals and communities can respond effectively to flood situations. The evaluation of the SVM algorithm's performance reveals an achieved accuracy of 66.66%. In conclusion, this study underscores the potential of the SVM algorithm for flood prediction in a mobile application. These findings contribute to the field of flood forecasting technology, paving the way for more sophisticated and effective flood prediction tools in the future.