A Flood Hazard Prediction and Risk Assessment Model Based on Machine Learning Approach

Abstract

With the acceleration of global climate change and the frequent intensification of human activities, the frequency and intensity of flood disasters are rising, and the global flood prevention situation is becoming more and more critical. Therefore, in-depth investigation of the causes of floods and their wide-ranging impacts has become an important issue that needs to be urgently addressed in the fields of environmental science and disaster management. Given the high cost of comprehensive data collection and processing of all potential flood prediction indicators, accurate selection of key indicators becomes the key to improve prediction efficiency. With the help of data analysis and machine learning techniques, this study aims to predict the flood risk level and the probability of flood occurrence through scientific methods, and to propose effective prevention and response strategies. In the selection of key indicators, this paper adopt a combination of strategies: on the one hand, the Spearman correlation coefficient and Cramér’s V are used to screen out the indicators that are closely related to the occurrence of floods; on the other hand, the risk of flood events is subdivided into high, medium, and low levels through K-means clustering method, and important features are further identified with the help of logistic regression, random forest, to construct the model for predicting and assessing the risks of floods. The results show that the optimized feature selection method, especially the application of the combined risk classification and random forest algorithm, not only significantly improves the prediction accuracy and generalization ability of the model, but also speeds up the model operation and enhances the interpretability of the decision-making process. This innovative method provides a more efficient and reliable technical support for flood risk assessment, disaster prevention and mitigation, which helps to reduce the negative impacts of flood disasters and safeguard people's lives and properties.