CAJ | 학술논문

以边坡为基本研究单元,经过主成分分析和独立性检验得到白龙江流域对滑坡形成贡献最大的6个因子：人口密度、坡度、坡向、断裂距离、岩性和高程.使用人工神经网络对白龙江流域进行滑坡敏感性评价,采用ROC曲线对模型精度进行验证.研究结果表明,人工神经网络能有效地对该区域进行滑坡敏感性评价,且能将研究区划分成5个区：极低危险区、低危险区、中等危险区、高危险区、极高危险区,各区面积占研究区面积的比例分别为9.53%,41.46%,12.12%,25.33%,11.58%.
이변파위기본연구단원,경과주성분분석화독립성검험득도백룡강류역대활파형성공헌최대적6개인자：인구밀도、파도、파향、단렬거리、암성화고정.사용인공신경망락대백룡강류역진행활파민감성평개,채용ROC곡선대모형정도진행험증.연구결과표명,인공신경망락능유효지대해구역진행활파민감성평개,차능장연구구화분성5개구：겁저위험구、저위험구、중등위험구、고위험구、겁고위험구,각구면적점연구구면적적비례분별위9.53%,41.46%,12.12%,25.33%,11.58%.
Slope units were used as the basic assessment units. Six conditional independent environmental factors were selected as the explanatory variables that contribute to landslide occurrence, i.e., elevation, slope, aspect, distance from fault, lithology and settlement density. Then, the methods of artificial neural network (ANN) were conducted for landslide hazard mapping. ROC curves were plotted as a means of evaluating the quality of the susceptibility zonations for the ANN model. The results show that ANN can effectively evaluate the hazards of landslides in the region. According to the result of the model, the study area could be classified into five categories, i.e., very high dangerous zone, high dangerous zone, moderate dangerous zone, low dangerous zone and very low dangerous zone, taking an area proportion of 9.53%, 41.46%, 12.12%, 25.33%, 11.58%, respectively.