Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation

Abstract

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).

Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation

“…Meanwhile, Alqadhia et al used particle swarm optimization (PSO), ANN, and other methods to study landslide susceptibility [15]. Husam et al used Ohe-X transformation to signifcantly improve the performance of ANN in landslide susceptibility evaluation [16]. On the contrary, Kalantar et al believed that SVM provided good classifcation accuracy in landslide susceptibility evaluation [17], while Saha et al asserted that SVM can solve regression analysis and classifcation issues and reduce the error rate [18].…”

Spatial and Temporal Variations of Land Use and Land Cover In Kadiri Watershed Using Rs & Gis Techniques

“…The land cover gets affected due to clearance of land for agriculture. Vegetation clearance exposes [4][5][6] soils to a greater risk of being eaten away by wind and water, especially on steep terrain, and once among the clouds, releases toxins into the atmosphere". "Land use/cover changes must be detected in order to have a better knowledge of landscape dynamics during the property management period.…”

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…Advances in remote sensing (RS) technologies and evolution in smart sensors and charge-coupled device cameras providing higher resolution, wider coverage, lower cost, continuous information, and timely revisiting mean that earth observation and its regular monitoring become viable [15][16][17][18]. RS is deployed in many applications such as disaster mapping [19][20][21][22], environment monitoring [23,24], land Journal of Sensors use/cover mapping [25][26][27][28][29][30], and forest mapping [31,32]. Due to improvement of spatial and temporal resolution of satellite imagery and availability of synthetic aperture radar (SAR) dataset, disaster mapping based on RS data has been converted into a hot topic [33,34].…”