Abstract
To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments.
Highlights
As frequently occurring geohazards in the world, landslides have features of slow movement but progressive deformation and destruction, often causing significantly severe damage in terms of losses both in human lives and properties
landslide susceptibility mapping (LSM) Acquired by random forest (RF) Model in the Study Area
The trained RF model was applied to the geospatial database to simulate the probability of landslides for each grid in the study area
Summary
As frequently occurring geohazards in the world, landslides have features of slow movement but progressive deformation and destruction, often causing significantly severe damage in terms of losses both in human lives and properties. Landslides mainly develop in mountainous areas and cause serious threats to environments, settlements, and industrial facilities. After sliding into the river, landslides can block the river, form natural dams, and cause floods. Landslides occurring in a reservoir can generate huge surges, turning over the dam and rushing downstream to destroy buildings, farmland, and roads. It is not easy to implement monitoring and defense measures; the losses tend to be extremely serious [1,2]
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