Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping

Tang, Rui-Xuan; E, Yan; Wen, Tao; Yin, Xiaomeng; Tang, Wei

doi:10.3390/su13073803

Cited by 25 publications

(19 citation statements)

References 80 publications

(152 reference statements)

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“…The deterministic model is based on the principle of slope instability and requires masses of known data, this method needs to be highly simplified and easy to analyze, and it is not suitable for the large-scale research of the LSP [11,12]. The nondeterministic model is based on statistical analysis, with the maturity of GIS technology and rapid computer development; simple algorithms include information model [13], weight-of-evidence model [14], and the analytic-hierarchy process [15]. With the rise of data mining, some more sophisticated algorithms have gradually been used in landslide-susceptibility research, such as the decision-tree model [16], support-vector-machine model [17], and artificial neural networks [18].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

et al. 2022

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Landslide susceptibility prediction (LSP) is the key technology in landslide monitoring, warning, and evaluation. In recent years, a lot of research on LSP has focused on machine learning algorithms, and the ensemble learning algorithm is a new direction to build the optimal prediction. Logistic model tree (LMT) combines the advantages of decision tree and logistic regression, which is smaller and more robust than ordinary algorithms. The main aim of this study is to construct and test LMT-based random forest (RF) and selected ensemble learning algorithms including bagging and boosting algorithms to compare their performance. Firstly, taking the county of Ziyang, China, as the study area, through historical reports, aerial-photo interpretations, and field investigations, 690 inventory maps of landslide locations were constructed and randomly divided into the 70/30 ratio for a training and validation dataset. Secondly, considering geological conditions, and landslide-induced disease and its characteristics, 14 landslide-conditioning factors was selected. Thirdly, the variance-inflation factor (VIF) and tolerance (TOL) were used to analyze the 14 factors, and the prediction ability was calculated with information-gain technology. Ultimately, the receiver-operating-characteristic (ROC) curve was applied to verify and compare model performance. Results showed that the LMT-RF model (0.897) was superior to other models, and the performance of LMT single model (0.791) was the worst. Therefore, it can be inferred that the LMT-RF model is a promising model, and the outcome of this study will be useful to planners and scientists in landslide sensitivity studies in similar situations.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

et al. 2022

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“…Through comparative analysis, it is found that the same main factors retained both by the LR ao model and the LR bo model include elevation, lithology, land cover, distance from roads, and POI kernel density. Among them, elevation and lithology represent the inoculation factors of landslides, which largely determine the stability of local slopes (Sivakumar and Ghosh, 2021;Tang et al, 2021). In the same way, the materials covered by the ground affect the slope surface, such as runoff and the accumulation of materials on the slope surface.…”

Section: Discussionmentioning

confidence: 99%

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

Sun

Wen

et al. 2021

Front. Earth Sci.

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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.

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“…The occurrence of a highway landslide disaster is closely related to topography, geological structure, hydrometeorology and human activities, and selecting suitable evaluation factors is the most important part of a landslide susceptibility evaluation, but due to the complexity of the influencing factors, there is no unified selection standard yet [27,28]. A highway, as a typical line-fitting engineering structure, has to traverse different topographic and geomorphological units, especially in mountainous areas, often spreading over slopes, valleys and mountain ranges.…”

Section: Selection Of Evaluation Factorsmentioning

confidence: 99%

A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model

Fan

Hu³

et al. 2022

Sustainability

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A landslide disaster, especially a highway landslide, may greatly impact the transport capacity of nearby roads. Keeping highways open, in particular, is crucial for supporting the functioning of the economy, society and people. Therefore, evaluating the highway landslide susceptibility is particularly important. In this paper, the city of Laibin, in the Guangxi Zhuang Autonomous Region of China, was taken as the study zone. According to data on 641 highway landslide disaster points measured in the field and a basic evaluation of the study area, nine evaluation factors—the elevation, slope, aspect, height difference, plan curve, profile curve, precipitation, Topographic Wetness Index (TWI) and vegetation coverage—were selected. We coupled a Frequency Ratio (FR) model, Analytic Hierarchy Process (AHP), Logistic Regression (LR), Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) to evaluate the susceptibility to highway landslides, with a Receiver Operating Characteristic (ROC) curve used to analyze the precision of these models. The ROC curve showed that the accuracy of the five models was greater than 0.700 and thus had a certain reliability. Among them, the FR-LR model had the highest accuracy, at 0.804. The study protocol presented here can therefore provide a reference for evaluation studies on landslide susceptibility in other areas.

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Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping

Cited by 25 publications

References 80 publications

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

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

A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model

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