机器学习模型在滑坡易发性评价中的应用现状与发展趋势OA

Landslide susceptibility evaluation is the necessary basis for landslide hazard evaluation and risk evaluation.Machine learning model can mine potential information in massive landslide disaster data,establish data connection,and find the essential law behind the land-slide phenomenon,which is one of the important means of current landslide susceptibility evaluation research.Based on the literature retrieval platform,this paper introduces the application status of different machine learning models in the field of landslide susceptibility evaluation,and discusses the advantages and disadvantages of machine learning models in the application of landslide susceptibility evaluation and the ap-plication situation,and puts forward the following understanding:①the coupled machine learning model is significantly better than the uncou-pled single machine learning model in landslide susceptibility evaluation,and the improved machine learning model is also significantly better than the unimproved single machine learning model in landslide susceptibility evaluation;②In the future,it is important to explore the mecha-nism of action,process and the interpretability of results of the"dark box"model algorithm,improve the accuracy and accuracy of landslide susceptibility evaluation,improve the shortcomings of model application research,and realize the nested coupling with artificial intelligence;③The characteristics and application of machine learning model are derived from its construction principle and run through the whole process of model application as inherent characteristics.Although machine learning model has received wide attention,people's understanding of it is still not deep enough.No consistent views and unified evaluation criteria have been formed yet,which needs further research.