A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

Abstract

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas.

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

“…The ADTree can be considered for classifying binary classes and enhancing the accuracy such that it has produced promising results in spatial prediction of landslide over the world [63,67,90,100]. It is a known fact that the ADTree is an interpretable and robust algorithm against noise in order to provide significant improvement in classification error in comparison to the individual/base decision tree stump classifiers [73].…”

“…Random subspace ensemble model comprises several classifiers in a data feature space. Random subspace ensemble classifier can be used by nearest neighbor, linear, support vector and by other classifiers [67]. The advantage of this model is that training data seems to be smaller for original data which is larger for subspace data.…”

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

“…Specifically, the combination of statistical and probabilistic models with Remote Sensing (RS) and GIS has been widely used by different researchers [1,12]. Additionally, some scientists and researchers have studied natural disasters, specifically floods and FSM, with the help of RS and GIS, using different models such as Decision-Tree (DT) [6,13], Support Vector Machine (SVM) [14,15], Frequency Ratio (FR) [16,17], Evidential Belief Function (EBF) [18][19][20], EBF-AHP (Analytical Hierarchy Process) [21], Logistic Regression (LR) [22], Shannon's entropy and weights-of-evidence [23], Artificial Neural Networks (ANN) [23], AHP [23,24], Random Forest [3,23], and Adaptive Neuro-Fuzzy Inference System (ANFIS) [25,26]. Recently, Khosravi et al [27] compared the prediction power of the data mining algorithms of Naïve Bayes and Naïve Bayes Tree with three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS, and SAW).…”

“…Their finding show that although MCDM models could predict flood-prone areas, the data mining algorithms had a higher prediction power than MCDMs since MCDMs rely on expert opinion. Arabameri et al [28] applied an EBF model to the generation of flood susceptibility maps and compared the results with FR, TOPSIS, and VIKOR models, concluding that the EBF model performed best.Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”

“…Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”