Research on the Prediction of Complex Systems Based on Integrated Machine Learning: A Case Study of the Probability of Flood Occurrence

With the aggravation of global climate change and human activities, the frequency and intensity of flood disasters have been continuously increasing, causing huge losses to human society. Accurately predicting the probability of flood occurrence is crucial for flood prevention and disaster reduction. This paper focuses on the uncertainty prediction of complex systems, taking the prediction of flood occurrence probability as a specific case. Integrated machine learning methods, combined with techniques such as correlation analysis, hierarchical clustering, and the entropy-weight TOPSIS method, are used to deeply analyze flood disaster data and construct an efficient prediction model. The research results show that the integrated machine learning model performs remarkably well in predicting the probability of flood occurrence, providing a scientific basis for the prevention and response to flood disasters. It also offers useful references for research in the field of uncertainty prediction of complex systems.