An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

Yanyan Li, Jianping Chen, Yanjun Shang
2016 Sustainability  
Assessing the failure potential of slopes is of great significance for land use and management. The objective of this paper is to develop a novel model for evaluating the failure probability of slopes based on a relevance vector machine (RVM), with a special attention to the characteristics of failed slopes along the lower reaches of the Jinsha River, close to the Wudongde dam site. Seven parameters that influence the occurrence of landslides were selected as environmental factors; namely
more » ... ctors; namely lithology, slope angle, slope height, slope aspect, slope structure, distance from faults, and land use. A total of 55 landslides mapped in the study area were used to train and test the RVM model. The results suggest that the accuracy of the model in predicting the failure probability of slopes, using both training and testing data sets, is very high and deemed satisfactory. To validate the model performance, it was applied to 28 landslide cases identified in the upper reaches of the Jinsha River, where environmental and geological conditions are similar to those of the study area. An accuracy of approximately 92.9% was obtained, which demonstrates that the RVM model has a good generalization performance. Hybrid methods, which are established by combining statistical approaches and artificial intelligence, have also been adopted for assessing geological hazards; these include artificial neural network (ANN)-Bayes analysis [14] , ANN-fuzzy logic [26] , and neuro-fuzzy inference systems [27, 28] . However the ANN-based approaches cannot provide objective and steady assessment results because their outcomes are operator dependent [13, 15] . The Bayes learning algorithm is considered to be an effective tool for knowledge representation and reasoning under the influence of uncertainty [10] . Based on this algorithm, a recently developed machine learning technique, relevance vector machine (RVM), was originally introduced by Tipping [29]. As a Bayesian treatment of the sparse learning problem, the RVM can yield a probabilistic output [30] . In this study, a novel empirical model for slope failure analyses based on RVM is presented. We selected the lower reaches of the Jinsha River close to the Wudongde dam site as the study area; 55 landslides mapped in the region were utilized to train and test the RVM model. To evaluate the validity of the model, it was applied to another landslide site where the environmental conditions are similar to those of the study area. Study Area The study area (Figure 1 ) lies along the lower reaches of the Jinsha River and is the reservoir region of the Wudongde hydropower station, which is located in the mountains separating the Sichuan and Yunnan provinces. The occurrence of landslides not only poses a threat to human lives and properties, but also affects the stability of the Wudongde dam. Thus assessing the failure potential of slopes in this area is of great significance.
doi:10.3390/su9010032 fatcat:hoj7r2el4na2vcdjlgrsbv6jti