Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

Abstract

Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area.

Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes)

“…From the digital elevation model (DEM), derivative models such as slope, aspect or orientation, terrain curvature, topographic position index (TPI) and terrain roughness index (TRI) have been obtained using QGIS analysis functions. Additionally, lithological units extracted from the Geologic Atlas of Colombia [69] were used, which were rasterized; then, a quantitative value was assigned to each unit based on material resistance [80], as shown in Table 3 (lower to hard rocks and higher to soft rocks). The NDVI and land use were obtained from a Sentinel-2 image using the corresponding formula [81] and supervised classification (maximum probability), respectively.…”

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

“…The general steps of model construction are as follows: First, randomly select n samples from the original data that have been replaced to form a sub-dataset (Bootstrap sampling); then, randomly select k attributes from the sub-dataset and select one of the optimal feature attributes as the partition node; next, repeat this step to build a sub-tree, and multiple sub-trees are integrated to form a random forest. The RF prediction result is determined by the optimal voting result of these decision trees [36].…”

Fast hybrid methods for modeling landslide susceptibility in Ardal County

“…Landslide susceptibility assessment is one of these fields that plays an imperative role in harnessing this hazard in prone areas 6 , 7 . As a prerequisite of many landslide susceptibility mapping approaches, a spatial database must be provided that contains the records of historical landslides, as well as relevant landslide conditioning factors (LCFs) 8 – 10 . Analyzing the spatial relationship between the landslide occurrence and different LCFs is the basis of studies 11 which provide a predictive landslide susceptibility map for a specific area within environments such as geographic information system (GIS) and Google earth engine (GEE) 12 , 13 .…”