机器学习算法在地质灾害风险评估中的应用与比较研究OA

The interweaving of complex topographic and geological conditions with high intensive human activities in China has made geological hazard risk assessment a core issue for safeguarding national territorial security and major engineering construction.Traditional assessment methods face bottlenecks in dealing with nonlinear coupling relationships among multiple factors,while machine learning(ML)algorithms,with their powerful pattern recognition and prediction capabilities,have brought new methodological breakthroughs to this field.This paper systematically reviews the application progress of algorithms such as logistic regression(LR),support vector machine(SVM),random forest(RF),gradient boosting machine(GBM),and deep learning(DL)in geological hazard susceptibility assessment.By comparing and analyzing the core principles,data requirements,predictive performance,stability and result interpretability of different algorithms,and combining typical cases at home and abroad,the advantages and applicable scenarios of various algorithms are clarified.The study has found that ensemble learning algorithms(e.g.,RF and GBM)exhibit higher accuracy and robustness in most cases;deep learning models(e.g.,convolutional neural networks,CNN)have great potential in automatic feature extraction,but their results have poor interpretability and high data volume requirements.The current application still face key challenges such as insufficient integration of model physical mechanisms,limited dynamic risk assessment capabilities,and poor regional transferability.This paper further discusses key technologies including feature engineering,sample processing,aa well as model optimization,and prospects the future development direction of integrating physical mechanism-driven and data-driven models,aiming to provide a systematic reference for the intelligent practice of engineering geological risk assessment.