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.
Highlights
Landslides are a devastating natural hazard, which cause an enormous loss of properties and human life [1,2,3]
The most important factors for landslide occuurence were determined based on the average merit (AM) metric of this method
A novel hybrid machine learning model, namely, multiboost based naïve bayes trees (MBNBT), was proposed for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province. This model is a combination of two effective machine learning techniques of the MB ensemble and the naïve bayes trees (NBT)
Summary
Landslides are a devastating natural hazard, which cause an enormous loss of properties and human life [1,2,3]. Landslide problems are still a great challenge to governmental agencies and hazard managers, as landslides are a phenomenon of high complexity [4]. Researchers throughout the world have attempted to establish relationships between past landslide occurrences and future landslides [8,9,10]. Landslide prediction studies are generally carried out through mathematical models by analyzing statistical relationships between the occurrences of past landslides and landslide affecting factors [11,12,13,14]. Several models and techniques have been proposed and applied for spatial prediction of landslides all over the world. These models include quantitative and qualitative models [15]. The most popular model among quantitative models is Logistic Regression (LR), which is based on statistical theory [16,17]
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