Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

• Published in: Applied sciences Vol. 9; no. 14; p. 2824
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2019
• Subjects: Algorithms; Bayesian analysis; ensemble techniques; Fault lines; goodness-of-fit; Intelligence; Landslides; Landslides & mudslides; Lithology; Machine learning; Mapping; Neural networks; Precipitation; Predictions; Rivers; Root-mean-square errors; Soft computing; Software; Support vector machines; Surface water; Topography; Trees; Vietnam; Vietnam
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9142824

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.