An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

Abstract

Abstract:The Mw 7.8 Gorkha earthquake of 25 April 2015 triggered thousands of landslides in the central part of the Nepal Himalayas. The main goal of this study was to generate an ensemble-based map of co-seismic landslide susceptibility in Sindhupalchowk District using model comparison and combination strands. A total of 2194 co-seismic landslides were identified and were randomly split into 1536 (~70%), to train data for establishing the model, and the remaining 658 (~30%) for the validation of the model. Frequency ratio, evidential belief function, and weight of evidence methods were applied and compared using 11 different causative factors (peak ground acceleration, epicenter proximity, fault proximity, geology, elevation, slope, plan curvature, internal relief, drainage proximity, stream power index, and topographic wetness index) to prepare the landslide susceptibility map. An ensemble of random forest was then used to overcome the various prediction limitations of the individual models. The success rates and prediction capabilities were critically compared using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). By synthesizing the results of the various models into a single score, the ensemble model improved accuracy and provided considerably more realistic prediction capacities (91%) than the frequency ratio (81.2%), evidential belief function (83.5%) methods, and weight of evidence (80.1%).

Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms

“…Therefore, in the present study, three classification algorithms (RF, XGBoost and DNN) were utilized and compared for landslide susceptibility mapping at Inje area. Basically, the RF model is one of the popular and widely used models to make landslide susceptibility [60,81,82] and on other hand, XGBoost and DNN had been explored in limited studies for spatial prediction of occurrence of the landslide, which showed promising results in the study area. Regard to important IFs, three models revealed that horizontal drainage proximity (h) is the most important IF among 13 IFs.…”

“…Previous studies illustrated that RF method can adequately depict probable occurrence of landslide and spatial data [81,82,86,87]. There are several pieces of research on landslide susceptibility using a gradient boosting algorithm [88][89][90].…”

Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest

“…Vulnerability assessments have been conducted for natural disasters other than earthquakes, including floods [22][23][24][25][26], landslides [27][28][29][30][31][32][33][34], gully erosion [35][36][37][38][39], and groundwater contamination [40][41][42][43][44]. Some studies have compared the performance of various methodologies, including probabilistic techniques such as frequency ratio (FR) models [22,27,43], statistical techniques such as LR-based models [22,27,28,32,34,38,[40][41][42][43], and machine learning algorithms such as decision tree (DT) [24,26,28,29,31,34,38,39,[42][43][44], random forest (RF) …”

Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea

“…In the past several years, many studies of natural disasters have implemented various methods to evaluate vulnerability and susceptibility, resulting in the development of risk-assessment maps. These methods have included probabilistic and statistical methodologies [15][16][17][18][19] and machine learning approaches [20][21][22][23][24][25][26][27]. Recent research has actively compared the suitability of machine-learning methodologies [28][29][30][31][32].…”