Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods

Abstract

Abstract:Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide prediction capability is highly important. The main objective of this study is to assess and compare the prediction capability of advanced machine learning methods for landslide susceptibility mapping in the Mila Basin (Algeria). First, a geospatial database was constructed from various sources. The database contains 1156 landslide polygons and 16 conditioning factors (altitude, slope, aspect, topographic wetness index (TWI), landforms, rainfall, lithology, stratigraphy, soil type, soil texture, landuse, depth to bedrock, bulk density, distance to faults, distance to hydrographic network, and distance to road networks). Subsequently, the database was randomly resampled into training sets and validation sets using 5 times repeated 10 k-folds cross-validations. Using the training and validation sets, five landslide susceptibility models were constructed, assessed, and compared using Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Artificial Neural Network (NNET), and Support Vector Machine (SVM). The prediction capability of the five landslide models was assessed and compared using the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC), overall accuracy (Acc), and kappa index. Additionally, Wilcoxon signed-rank tests were performed to confirm statistical significance in the differences among the five machine learning models employed in this study. The result showed that the GBM model has the highest prediction capability (AUC = 0.8967), followed by the RF model (AUC = 0.8957), the NNET model (AUC = 0.8882), the SVM model (AUC = 0.8818), and the LR model (AUC = 0.8575). Therefore, we concluded that GBM and RF are the most suitable for this study area and should be used to produce landslide susceptibility maps. These maps as a technical framework are used to develop countermeasures and regulatory policies to minimize landslide damages in the Mila Basin. This research demonstrated the benefit of selecting the best-advanced machine learning method for landslide susceptibility assessment.

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

“…More importantly, this study highlights the consideration of slope factor (CF = 1 for class 39°-55°) as an important variable in the Japanese mountainous terrains while modeling the landslide susceptibility. This also confirms the trend noted by Oguchi [73].…”

“…This is because both SVM and Remote Sens. 2019, 11, 638 25 of 30 ANN have high capacities to deal with non-linear and complex problems as landslides as confirmed in various previous studies [60,73]. In contrast to the SVM model and the ANN model, three statistical models (PLFR, InV and CF) have difficulties to model the complex landslides of the Chuetsu region.…”

Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh

“…In other words, susceptibility is related to spatial aspects of the hazard [41]. LSMs have been widely accepted and have become more popular with the advancement of geographic information system (GIS) and remote sensing (RS) tools and techniques, and availability of satellite images [42][43][44]. A variety of data-driven bivariate and multivariate statistical methods-the weights of evidence (WoE), logistic regression, and multiple linear regression (MR); machine learning methods-the random forest, artificial neural network (ANN), support vector machine (SVM); and user-defined weight based multi-criteria evaluation methods such as the analytic hierarchy process (AHP), weighted linear combination (WLC), and ordered weighted average (OWA), are being effectively implemented in the LSMs [10,12,[45][46][47].…”

Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling

“…Some related works have also considered using various composite strategies based on previous approaches to achieve a specific purpose [45,47,54]. A number of studies in the literature have compared different methods and various results obtained from modeling landslide susceptibility for different study sites and collected data [16,30,35,38,[42][43][44][55][56][57]. However, no general consensus has yet been reached concerning which is the best procedure and algorithm for evaluating landslide susceptibility [57,58].…”