Prediction of Flood by Rainf All Using MLP Classifier of Neural Network Model

Floods are powerful devastating natural hazards and upgrading them is a risky task. The system development of predicting the flood helps in risk reduction, policy recommendations, a reduction in human life loss, and a reduction in property harm, all of which are correlated with floods. During the last two decades, neural network approaches have made significant contributions to the enhancement of predicting the flood by including the dynamic mathematical equations of physical flood processes. This has resulted in improved performance and cost-effective solutions. To overcome these issues and forecast the floods based on rainfall neural networks technique are used. Dealing with variable creation, missing value treatments, data cleaning/preparation, exploratory review, and assessment was all part of the analysis process. Many algorithms are used for predicting the flood such as k-means clustering, support vector machine, MLP classifier. Among them MLP produces the good accuracy and displays output in Graphical user Interface.