Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

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ID: 261937
2019
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Abstract
We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas.
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nguyen2019applieddevelopment1 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Phong Tung Nguyen;Tran Thi Tuyen;Ataollah Shirzadi;Binh Thai Pham;Himan Shahabi;Ebrahim Omidvar;Ata Amini;Hersh Entezami;Indra Prakash;Tran Van Phong;Thao Ba Vu;Tran Thanh;Lee Saro;Dieu Tien Bui;Nguyen, Phong Tung;Tuyen, Tran Thi;Shirzadi, Ataollah;Pham, Binh Thai;Shahabi, Himan;Omidvar, Ebrahim;Amini, Ata;Entezami, Hersh;Prakash, Indra;Phong, Tran Van;Vu, Thao Ba;Thanh, Tran;Saro, Lee;Bui, Dieu Tien;
Journal applied sciences
Year 2019
DOI
10.3390/app9142824
URL
https://dx.doi.org/10.3390/app9142824
Keywords
Machine learning goodness-of-fit vietnam landslides ensemble techniques

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