Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China

Abstract

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features.

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

“…Because there is no universally accepted framework for determining independent variables in LSM, the choice of LCFs was guided by a comprehensive review of the relevant literature, studyarea-specific data, and field investigations [53]. The current study employed twelve LCFs featuring a range of variables, including elevation, land use/land cover (LULC), lithology, distance to faults, rivers, and roads, curvature, "topographic wetness index (TWI)", aspect, rainfall, and slope [54][55][56] (refer to Table 1). Figures 4 and 5 depict the creation of thematic layers with 12.5 × 12.5 m pixel size conducted within the WGS84 Datum and UTM-Zone 43 coordinate system.…”

Fast hybrid methods for modeling landslide susceptibility in Ardal County

“…As for landslide hazard assessment, machine learning models can explore how historical landslides occurred under the effect of LCFs, and accordingly, predict landslide susceptibility values (V LS ) to generate the maps. Artificial neural network 28 , random forest 29 , regression models 30 , support vector models 31 , and fuzzy-based models 32 are prevalent machine learning methods that have been used for landslide susceptibility modeling in different parts of the globe. However, many scholars have suggested to enhance the efficiency of these models by incorporating optimization algorithms, a.k.a metaheuristic algorithms 33 , 34 .…”