GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Abstract

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

“…Validation performance is a critical step in a modeling procedure, for which several statistical indices has been suggested and used [13,14,[49][50][51][52]. In this study, we used Area Under Receiver Operating Characteristic (ROC) curve (AUC) [39,[53][54][55][56], Root Mean Squared Error (RMSE) [57][58][59][60][61][62][63][64], Kappa, Accuracy (ACC), Specificity (SPF), Sensitivity (SST), Negative predictive value (NPV), and Positive predictive value (PPV) [65][66][67][68][69]. Detail description of these indices is presented in published literature [61,[70][71][72][73][74][75][76][77].…”

Using Analytical Hierarchy Process and Multi-Influencing Factors to Map Groundwater Recharge Zones in a Semi-Arid Mediterranean Coastal Aquifer

“…An important step for any model is validation to assess the performance by establishing the relationship between groundwater recharge level class and GWRZ map [76][77][78][79]. Several methods can be used to validate groundwater recharge maps, such as receiver performance analysis, curve area, groundwater yield estimation of wells during field visits and a comparative study between the water level profile and groundwater recharge zones, among others [78,[80][81][82]. In this study, two methods were used to validate the obtained groundwater recharge zones, namely: Receiver Operating Characteristics (ROC) curve and groundwater level monitoring and assessment at 25 shallow wells.…”

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

“…The area under this curve is called the AUC, and the model with the highest AUC has the highest relative performance [52][53][54][55][56][57][58][59][60][61]. The AUC values equal to 0.5 indicate random prediction for a model [62][63][64][65][66].…”