基于深度信念网络的滑坡敏感性评价

滑坡敏感性评价中各致灾因子之间存在复杂非线性关系，传统的评价模型难以揭示该类复杂关系，以致评价结果精度受限.基于文献调查与实地调研，选取高程、地貌类型、岩性、坡度、与构造线距离、与水系距离和年均降雨量为主要致灾因素，在地理信息系统(GIS)中建立了基于深度信念网络(DBN)模型的区域滑坡敏感性区划模型，并以四川区域为例进行了实例分析.最后通过ROC曲线特征将评价结果与逻辑回归(LR)和人工神经网络(BPNN)模型评价结果进行了对比分析，并探讨了各评价模型对不同致灾因子的响应.研究表明DBN模型具有更高精度以及较低的假阳性率和假阴性率，更适合于大区域、复杂致灾因素的区划滑坡敏感性评价工作.

LandslidesSusceptibilityAssessmentBasedonDeepBeliefNetwork

Complex non-linear relationships exist among causing factors in landslides susceptibility assessment. Traditional assessment models is difficult to reveal such complex relationships， and limit the accuracy of assessment results. Based on literature review and field survey， the altitude， landform， lithology， slope， distance to tectonic line， distance to drainage network and annual average rainfall were chosen as the main causing factors. A regional landslides susceptibility mapping model based on deep belief network (DBN) model in geographic information system (GIS) was established， and Sichuan Province was taken as an example. Finally， through the characteristics of ROC curves， the assessment results were compared with logistic regression (LR) and BP neural network (BPNN)， and the response of each model to different causing factors was discussed. The results show that the DBN model has high accuracy， low false positive rate and false negative rate， and is suitable for landslides susceptibility assessment in large area with complex causing factors.