This paper introduces three machine learning (ML) algorithms, the 'ensemble' Random Forest (RF), the 'ensemble' Gradient Boosted Regression Tree (GBRT) and the MultiLayer Perceptron neural network (MLP) and applies them to the spatial modelling of shallow landslides near Kvam in Norway. In the development of the ML models, a total of 11 significant landslide controlling factors were selected. The controlling factors relate to the geomorphology, geology, geo-environment and anthropogenic effects: slope angle, aspect, plan curvature, profile curvature, flow accumulation, flow direction, distance to rivers, total water content, saturation, rainfall and distance to roads. It is observed that slope angle was the most significant controlling factor in the ML analyses. The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic (ROC) analysis. The results show that the 'ensemble' GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides, with a 95% probability of landslide detection and 87% prediction efficiency.

本文介绍了三种机器学习（ML）算法，即“集合”随机森林（RF），“集合”梯度提升回归树（GBRT）和多层感知器神经网络（MLP），并将它们应用于浅层空间建模挪威克瓦姆附近的山体滑坡。在ML模型的开发中，总共选择了11个重要的滑坡控制因素。控制因素与地貌，地质，地质环境和人为影响有关：坡度，纵横比，平面曲率，剖面曲率，流量积聚，水流方向，与河流的距离，总含水量，饱和度，降雨和距道路的距离。可以看出，倾斜角是ML分析中最重要的控制因素。三种ML模型的性能是根据接收器工作特性（ROC）分析进行定量评估的。结果表明，“整体式” GBRT机器学习模型对浅层滑坡的空间预测产生了最有希望的结果，滑坡检测的可能性为95％，预测效率为87％。