Prediction of Flash Flood using Rainfall by MLP Classifier

Abstract

Flood are one of the unfavorable natural disasters. A flood can result in a huge loss of human lives and properties. It can also affect agricultural lands and destroy cultivated crops and trees. The flood can occur as a result of surface-runoff formed from melting snow, long-drawn-out rains, and derisory drainage of rainwater or collapse of dams. Today people have destroyed the rivers and lakes and have turned the natural water storage pools to buildings and construction lands. Flash floods can develop quickly within a few hours when compared with a regular flood. Research in prediction of flood has improved to reduce the loss of human life, property damages, and various problems related to the flood. Machine learning methods are widely used in building an efficient prediction model for weather forecasting. This advancement of the prediction system provides cost-effective solutions and better performance. In this paper, a prediction model is constructed using rainfall data to predict the occurrence of floods due to rainfall. The model predicts whether “flood may happen or not” based on the rainfall range for particular locations. Indian district rainfall data is used to build the prediction model. The dataset is trained with various algorithms like Linear Regression, K- Nearest Neighbor, Support Vector Machine, and Multilayer Perceptron. Among this, MLP algorithm performed efficiently with the highest accuracy of 97.40%. The MLP flash flood prediction model can be useful for the climate scientist to predict the flood during a heavy downpour with the highest accuracy.