Research on natural flood disaster prediction and risk assessment based on data analysis and machine learning

With the intensification of global climate change, the frequency and intensity of natural floods are increasing, causing significant impacts on human society, economy, and environment. Traditional flood prediction and risk assessment methods often rely on physical models, which have limitations such as high data requirements, complex computational processes, and poor real-time performance. This paper aims to use data analysis and machine learning techniques to build efficient flood disaster prediction and risk assessment models, thereby improving prediction accuracy and evaluation efficiency, providing a scientific basis for flood disaster management. By introducing an improved K-means clustering method and a random forest classifier, precise classification and feature selection of flood event risks have been achieved. A flood disaster prediction model based on multiple machine learning algorithms has been constructed, and its effectiveness and reliability have been verified through practical case studies.