Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow
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ORIGINAL PAPER
Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping Zhu Liang1 • Changming Wang1 • Kaleem Ullah Jan Khan1 Accepted: 30 September 2020 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The existence of shallow landslide brings huge threats to the human lives and economic development, as the Lang County, Southeastern Tibet prone to landslide. Landslide susceptibility mapping (LSM) is considered as the key for the prevention of hazard. The primary goal of the present study is to assess and compare four models: classification and regression tree, gradient boosting decision tree (GBDT), adaptive boosting-decision tree and random forest for the performance of landslide susceptibility modeling. Firstly, a landslide inventory map consisting of 229 historical shallow landslide locations was prepared and the same number of non-landslide points was determined by k-means clustering. Secondly, 12 conditioning factors were considered in the landslide susceptibility modeling. The prediction performance of the four models were estimated by fivefold cross validation and relative operating characteristic curve (ROC), area under the ROC curve (AUC) and statistical measures. The results showed that the GBDT performed best in the training and validation dataset, with the highest prediction capability (AUC = 0.986 and 0.940), highest accuracy value (95.3% and 88.1%) and highest kappa index (0.904 and 0.772), respectively. Therefore, the GBDT was considered to be the most suitable model and applied to the whole study area for LSM. The results of this study also demonstrate that the performance can be enhanced with the use of ensemble learning. The sampling strategy of non-landslide points can be improved by combining with clustering analysis which are more reasonable. Keywords Shallow landslide Susceptibility Ensemble learning K-means clustering
1 Introduction In geomorphology, a ‘‘landslide’’ is the movement of a mass of rock, debris or earth down a slope, under the influence of gravity (Cruden and Varnes 1996). According to different variables, landslides can be divided into different types (Varnes 1978). Rainfall-induced shallow landslides is a natural phenomenon mainly occurring in mountainous areas which are widespread all over the world and caused damages on both human lives and economy directly or indirectly (Trigila and Iadanza 2012). Generally, damages can be decreased to a certain extent by predicting
& Changming Wang [email protected] 1
College of Construction Engineering, Jilin University, Changchun 130000, People’s Republic of China
the likely location of future disasters (Pradhan 2010). Therefore, landslide susceptibility prediction is the first step towards estimation or reduction of landslide hazard and risk. The effectiveness of LSM depends greatly on the modeling methodology adopted. The approaches of LSM can be broadly classified as qualitative and quantitative (Liang et al
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