This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area of Van Chan district, Yen Bai province (Vietnam) was carried out with the help of a spatial database of the area, considering past landslides and 12 landslide conditioning factors. The proposed models were validated using different methods such as Area under the Receiver Operating Characteristics (ROC) curve (AUC), Mean Square Error (MSE), Root Mean Square Error (RMSE). Results indicate that the RFBFDT (AUC = 0.826, MSE = 0.189, and RMSE = 0.434) is the best method in comparison to other hybrid models, namely PSOANFIS (AUC = 0.76, MSE = 0.225, and RMSE = 0.474) and PSOANN (AUC = 0.72, MSE = 0.312, and RMSE = 0.558). Thus, it is reasonably concluded that the RFBFDT is a promising hybrid machine learning approach for landslide susceptibility modeling.

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.

No abstract

No abstract

No abstract