@article{ref1,
title="The influence of different knowledge-driven methods on landslide susceptibility mapping: a case study in the Changbai Mountain Area, Northeast China",
journal="Entropy (Basel, Switzerland)",
year="2019",
author="Ma, Zhongjun and Qin, Shengwu and Cao, Chen and Lv, Jiangfeng and Li, Guangjie and Qiao, Shuangshuang and Hu, Xiuyu",
volume="21",
number="4",
pages="e372-e372",
abstract="Landslides are one of the most frequent geomorphic hazards, and they often result in  the loss of property and human life in the Changbai Mountain area (CMA), Northeast  China. The objective of this study was to produce and compare landslide  susceptibility maps for the CMA using an information content model (ICM) with three  knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM),  the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM  (RS-ICM)) and to explore the influence of different knowledge-driven methods for a  series of parameters on the accuracy of landslide susceptibility mapping (LSM). In  this research, the landslide inventory data (145 landslides) were randomly divided  into a training dataset: 70% (81 landslides) were used for training the models and  30% (35 landslides) were used for validation. In addition, 13 layers of landslide  conditioning factors, namely, altitude, slope gradient, slope aspect, lithology,  distance to faults, distance to roads, distance to rivers, annual precipitation,  land type, normalized difference vegetation index (NDVI), topographic wetness index  (TWI), plan curvature, and profile curvature, were taken as independent, causal  predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM,  AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC)  method. The success accuracies of the landslide susceptibility maps produced by the  ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883,  respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for  the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that  the four models used in this study gave close results, with the RS-ICM exhibiting  the best performance in landslide susceptibility mapping.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e21040372",
url="http://dx.doi.org/10.3390/e21040372"
}