Flood Prediction Using ML Classification Methods on Rainfall Data

Abstract

Abstract: Floods are among the most destructive, most complex natural disasters to mimic. Research on the development of flood forecast models has contributed to risk reduction, a policy proposal, reduction of human lives, and mitigation of flood-related property damage. To mimic the complex statistical manifestations of natural flood processes, over the past two decades, neural network approaches have contributed significantly to the development of predictive systems that provide better performance and cost-effective solutions. To prevent this problem predict the occurrence of floods or not with a rain database by investigating neural network-based strategies. Database analysis by Multi-Layer Perceptron Classifier (MLP) to capture a number of details such as dynamic identification, deficit treatment, data validation, and data cleaning/preparation will be done across the given database. To apply flood forecasts for or without accurate calculation in the class division report, find the confusion matrix and the result shows the efficiency of the python frame-based flask based on the given attributes. Keywords: Flood forecast, decision tree, Random Forest, Descent, SVM, Flasks.