Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Abstract

This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, was selected as the study area since it experienced 1,522 landslides from 2001 to 2016. Second, 22 factors were selected as the initial conditioning factors, and a geospatial database was established with a grid of 30 m precision. Factor detection of the geographic detector and the stepwise regression method included in logistic regression were used to screen out the dominant factors from the database. Then, based on the sample dataset with a 1:10 ratio of landslides and nonlandslides, 10-fold cross validation was used to select the optimized sample to train the logistic regression model of landslide susceptibility in the study area. Finally, the accuracy and efficiency of the two models before and after screening out the dominant factors were evaluated and compared. The results showed that the total accuracy of the two models was both more than 0.9, and the area under the curve value of the receiver operating characteristic curve was more than 0.8, indicating that the models before and after screening factor both had high reliability and good prediction ability. Besides, the screened factors had an active leading role in the geospatial distribution of the historical landslide, indicating that the screened dominant factors have individual rationality. Improving the geospatial agreement between landslide susceptibility and actual landslide-prone by the screening of dominant factors and the optimization of the training samples, a simple, efficient, and reliable logistic-regression–based landslide susceptibility model can be constructed.

A quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism

“…In this research, landslide units and non-landslide units formed the sample dataset for the experiment, and the landslide units consisted of 952 historical landslide events. We finally chose a ratio of 1:10 to construct non-disaster negative sample points after several experiments (Sun et al, 2021). Moreover, in order to select truly "non-landslides" as closely as possible, the 500 m buffer zone of historical landslide points and major river system were avoided.…”

Experimental and Numerical Studies on Flowing Properties of Grouting Mortar Based on the Modified MPS Method

“…Li et al [17] and Jing-jun et al [18] used the MPS method to simulate the flow characteristics of SCC and RFC in the L-box test. The results show that this method can simulate the flow process of SCC in rock-filled bodies, predict the compactness of RFC, and ultimately provide a reference for the design and construction practice of RFC engineering [19][20][21]. The MPS method was also used to investigate the geohazard mechanism [22,23] and flow performances of concrete [24,25].…”

Interpretable Landslide Susceptibility Evaluation Based on Model Optimization

“…To address this need, Landslide Susceptibility Mapping (LSM) was developed as an important tool for the systematic identification of areas prone to landslides [4]. LSM provides a foundation for decision-making in land use planning and emergency management [5]. In recent years, the application of machine learning (ML) in LSM has increased due to its ability to achieve high prediction accuracy using various models [6], such as Logistic Regression (LR) [7], Artificial Neural Networks (ANN) [8], Random Forest (RF) [9], and Support Vector Machines (SVM) [10], among others.…”