Geological Hazard Susceptibility Prediction Based on Decision Tree Algorithm

The occurrence of geological hazards poses a serious threat to human life and property security, making the accurate and effective prediction of such events of paramount importance. Accurately predicting the susceptibility of geological hazards can facilitate early warning and help implement corresponding emergency measures. This study is based on the decision tree algorithm and focuses on the analysis of the susceptibility of geological hazards. A predictive model for the susceptibility of geological hazards based on decision tree is constructed. Furthermore, appropriate feature selection and weight analysis methods are employed, considering the issue of imbalanced classes in the actual samples, to optimize the model and obtain a better-performing predictive model. Experimental results demonstrate that the proposed model can effectively identify key factors influencing the susceptibility of geological disasters and accurately predict the likelihood of potential hazard occurrences. This research outcome provides a reliable tool and method for the prevention and management of geological disasters, aiding in the formulation of more scientifically effective management policies and measures.