陈涛, 钟子颖, 牛瑞卿, 刘桐, 陈胜云. 利用深度信念网络进行滑坡易发性评价[J]. 武汉大学学报 ( 信息科学版), 2020, 45(11): 1809-1817. DOI: 10.13203/j.whugis20190144
引用本文: 陈涛, 钟子颖, 牛瑞卿, 刘桐, 陈胜云. 利用深度信念网络进行滑坡易发性评价[J]. 武汉大学学报 ( 信息科学版), 2020, 45(11): 1809-1817. DOI: 10.13203/j.whugis20190144
CHEN Tao, ZHONG Ziying, NIU Ruiqing, LIU Tong, CHEN Shengyun. Mapping Landslide Susceptibility Based on Deep Belief Network[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1809-1817. DOI: 10.13203/j.whugis20190144
Citation: CHEN Tao, ZHONG Ziying, NIU Ruiqing, LIU Tong, CHEN Shengyun. Mapping Landslide Susceptibility Based on Deep Belief Network[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1809-1817. DOI: 10.13203/j.whugis20190144

利用深度信念网络进行滑坡易发性评价

Mapping Landslide Susceptibility Based on Deep Belief Network

  • 摘要: 以三峡库区秭归县郭家坝镇为研究区,从多源数据中提取12类滑坡评价因子,在采用网格单元作为评价单元的基础上,利用深度信念网络(deep belief network,DBN)对该区域的滑坡易发性进行评价,生成了该区域的滑坡易发性分区图,并与浅层神经网络和传统的逻辑回归方法进行比较, 采用受试者工作特征曲线、总体精度和Kappa系数3种精度评价方法对结果进行评价。结果表明,基于深度信念网络的滑坡易发性评价模型具有较好的稳定性,其预测结果的曲线下面积、总体精度、Kappa系数在3个模型中最优,分别为0.95、90%和0.81,表明DBN在滑坡易发性评价中具有较好的预测能力。

     

    Abstract:
      Objectives  Landslides are very harmful natural geological phenomenon.They are widely developed in mountainous areas in China, especially the Three Gorges Reservoir area. At present, more than 3 800 landslides have been reported. However, it is difficult to evaluate the landslide susceptibility and improve the accuracy of the model for areas with the same/similar geological background scientifically and accurately. The main objective of this study is to produce landslide susceptibility maps for the Guojiaba Town of Zigui County at the Three Gorges area, China by using deep belief network (DBN) method, and to explore its application effect in landslide susceptibility mapping.
      Methods  The landslide susceptibility mapping method is DBN. It is a superposition of multiple restricted Boltzmann machines (RBMs), which can better reflect the relationship among factors in order to obtain better results. Firstly, multi-source data is used to extract the factors affecting the landslide development in the study area. By analyzing the correlation between each factor and historical landslides, 12 evaluation factors that have a large impact on landslide development are selected, and they are quantified and normalized. Then, the grid unit is selected as the smallest unit for landslide susceptibility mapping, and the obtained sample units are selected and divided. 30% of all landslide data and the same amount of non-landslide data is selected randomly to form a training sample set. 70% of all landslide data and the same amount of non-landslide data as the validation sample set; third, the obtained training sample set is input to the deep belief network for training, and the validation sample set is used for model verification. In order to test the stability of the proposed model, with the same sample and parameter settings, five experiments are carried out. Finally, the same training set and validation set are used to evaluate the landslide susceptibility using shallow neural networks and logistic regression models.The results of the three methods are evaluated by using ROC(receiver opera- ting characteristics) curve, OA (overall accuracy) and Kappa coefficient.
      Results  The proposed DBN model is used to calculate based on the evaluation units in the study area, and the probability values corresponding to the evaluation units are obtained. The susceptibility of landslides in the study area is divided into four classes which are non-prone, low-prone, moderate-prone and high-prone area. The zonation map of landslide susceptibility based on DBN is obtained. From the results of the stability test of DBN, the average accuracy of DBN constructed in the mapping of landslide susceptibility is 91.14%, and the mean square error is 1.94%, which proves that the method has better performance of stability. By comparing with the mapping results obtained by the shallow neural network and logistic regression model, it is found that DBN has higher accuracy on the three evaluation indexes of ROC curve, OA and Kappa coefficient, which are 0.95, 90% and 0.81, followed by the shallow neural network model, of which are 0.92, 88%, and 0.76. The accuracies of logistic regression model are the lowest, of which are 0.86, 78%, and 0.55, respectively.
      Conclusions  A DBN is used to quantitatively mapping the landslide susceptibility in Guojiaba Town, Zigui County, the Three Gorges Reservoir area, and a regional landslide susceptibility map is generated.Compared with the shallow neural network and traditional logistic regression method, the accuracy assessment of the results is performed using three accuracy evaluation indexes, which are the receiver's working characteristic curve, OA, and Kappa coefficient. The results show that DBN obtains good stability, and the area under the curve, OA, and Kappa values are the highest among the three models, indicating that DBN performs a good predictive ability and stability in the mapping of landslide susceptibility.

     

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