Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam

Quoc Cuong Tran,Indra Prakash,Duy Huu Nguyen,Nadhir Al-Ansari,Hiep Van Le,Duc Do Minh,Duc Anh Nguyen,Duc Tung Van,Abolfazl Jaafari,Binh Thai Pham,Duc Dao Minh,Trung Hieu Tran,Lanh Si Ho

doi:10.3390/app10113710

Abstract

Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.

Highlights

Landslides are global geohazards that are responsible for substantial death and injury [1], as well as damage to the natural and built environment [2]
To assess and compare the ability of the six models proposed in this study, the following quantitative statistic measures were used: positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), Kappa index, root mean square error (RMSE), and area under the receiver operating characteristic (ROC) curve (AUC)
The results revealed that distance from roads with average merit (AM) = 82.379 was the most useful variable for describing the distribution of landslide susceptibility in the Nam

Summary

Introduction

Landslides are global geohazards that are responsible for substantial death and injury [1], as well as damage to the natural and built environment [2]. Landslide susceptibility can be modeled using a variety of GIS-aided methods, including regression logistics [6], simplified statistical/probabilistic frequency ratios [7], analytical hierarchy process [8], statistical indices [9], weight of evidence [10], evidential belief functions [11], certainty factors [12], and geographically weighted regression [13] Another suite of approaches is machine learning methods, which are known as methods with an advantage for processing large datasets that exhibit non-linear and complex relationships, and that are typically associated with environmental problems, natural hazard issues such as floods [14], wildfires [15], sinkholes [16], drought [17], earthquakes [18], gully erosion [19], and land subsidence [20,21]. The methods can recognize the discrepancy between historical records and different landscape-level variables to predict future events [22]

Methods

Results

Conclusion