scholarly journals Modeling Spatial Landslide Probability in Volcanic Terrains Through Continuous Neighborhood Spatial Analysis and Multiple Logistic Regression in La Ciénega Watershed, Nevado de Toluca, Mexico

2021 ◽  
Author(s):  
Rutilio Castro-Miguel ◽  
Gabriel Legorreta Paulín ◽  
Roberto Bonifaz-Alfonzo ◽  
José Fernando Aceves Quesada ◽  
Miguel Ángel Castillo-Santiago
Keyword(s):  
Logistic Regression ◽  
Spatial Analysis ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Spatial Prediction ◽  
Sampling Point ◽  
Multiple Logistic Regression ◽  
Distance Ratio ◽  
Nevado De Toluca

Abstract Little work has been done on the effect of the pixel neighborhood information when modeling landslide susceptibility using Multiple Logistic Regression (MLR). This research uses in situ and neighborhood cartographic information to evaluate how pixel distance of sampling sites affects the precision and accuracy of the MLR landslide susceptibility model. Two landslide susceptibility models are used: MLR-in situ, calibrated and validated by using variables that are collected at the site of the sampling point; and MLR in combination with Continuous Neighborhood Spatial Analysis (CNSA) to incorporate a search radius to extract pixel values for each cartographic variable based on a distance ratio. La Ciénega watershed on the eastern flank of the volcano Nevado de Toluca is selected as a study area. Its climate, topography, geomorphology, and geology predispose it to episodic landslides. The resulting susceptibility maps are validated in terms of the area under the curve (AUC) of the receiver operating characteristic (ROC), and they are compared with an inventory map in a contingency table; the MLR-CNSA model yields the better spatial prediction and representation of landslide susceptibility. The AUC evaluation indicates a predictive capability for the MLR-CNSA model of 0.969.

scholarly journals Landslide susceptibility mapping on a global scale using the method of logistic regression

2017 ◽  
Vol 17 (8) ◽  
pp. 1411-1424 ◽  
Author(s):  
Le Lin ◽  
Qigen Lin ◽  
Ying Wang
Keyword(s):  
Logistic Regression ◽  
Soil Moisture ◽  
Landslide Susceptibility ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Global Scale ◽  
Landslide Susceptibility Mapping ◽  
Landslide Occurrence ◽  
Explanatory Factors ◽  
Five Factors

Abstract. This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

scholarly journals Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Geosciences ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 430 ◽  
Author(s):  
Sangey Pasang ◽  
Petr Kubíček
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Information Value ◽  
Weight Of Evidence ◽  
Regression Methods ◽  
Susceptibility Maps ◽  
Road Corridors ◽  
Landslide Susceptibility Maps ◽  
Control Samples

In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway.

Investigate the Influence of Hydraulic Factors on Landslide Susceptibility of Riverbank for the Chenyulan watershed

2020 ◽  
Author(s):  
Xiao-Zhu Hong ◽  
Po-An Chen ◽  
Hsun-Chuan Chan
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Sediment Supply ◽  
Upstream Region ◽  
Sinuosity Index ◽  
Topographic Slope ◽  
Auc Value ◽  
Lateral Erosion

<p>The riverbank landslide is considered as the major sediment supply in the watershed. It mostly due to the river flows erode the foot of the riverbank, which makes the slope unstable. This study focused on the watershed susceptibility analysis of the riverbank landslide in the Chenyulan watershed. The Logistic regression method was used to establish the landslide susceptibility model not only with the topography, geological and anthropic factors, but also with the hydraulic factors including the hydraulic Sinuosity index, channel gradient, and concave-or-convex bank. The study areas were classified into four regions, according to the river-bed slope and confluence of rivers. The effects of the hydraulic factors on the model results were investigated. In the upstream region with mild topographic slope, the landslides were found to be dominated by the topography factors. The area under the curve (AUC) value of the model was 74.2%. In the upstream region with steep topographic slope, the steep hillslopes and the channel erosion of the concave bank produced a high weight of concave-or-convex bank in the model. The developed model exhibited an increased AUC value of 77.2%. In the downstream region, the lateral erosion of the channel increased the weights of hydraulic sinuosity index and channel gradient in the model. The developed model exhibited high area under the curve (AUC) value of 89.2%. The hydraulic factors increased the predictive performance of the model considerably.</p><p>Keyword: Riverbank, Hydraulic factors, Logistic regression</p>

scholarly journals Artificial Neural Networks Versus Multiple Logistic Regression to Predict 30-Day Mortality After Operations For Type A Ascending Aortic Dissection§

2009 ◽  
Vol 3 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Francesco Macrina ◽  
Paolo Emilio Puddu ◽  
Alfonso Sciangula ◽  
Fausto Trigilia ◽  
Marco Totaro ◽  
...  
Keyword(s):  
Risk Factors ◽  
Neural Networks ◽  
Logistic Regression ◽  
Aortic Dissection ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Type A ◽  
Multiple Logistic Regression ◽  
Short Term Mortality ◽  
Potential Risk Factors

Background:There are few comparative reports on the overall accuracy of neural networks (NN), assessed only versus multiple logistic regression (LR), to predict events in cardiovascular surgery studies and none has been performed among acute aortic dissection (AAD) Type A patients.Objectives:We aimed at investigating the predictive potential of 30-day mortality by a large series of risk factors in AAD Type A patients comparing the overall performance of NN versus LR.Methods:We investigated 121 plus 87 AAD Type A patients consecutively operated during 7 years in two Centres. Forced and stepwise NN and LR solutions were obtained and compared, using receiver operating characteristic area under the curve (AUC) and their 95% confidence intervals (CI) and Gini’s coefficients. Both NN and LR models were re-applied to data from the second Centre to adhere to a methodological imperative with NN.Results:Forced LR solutions provided AUC 87.9±4.1% (CI: 80.7 to 93.2%) and 85.7±5.2% (CI: 78.5 to 91.1%) in the first and second Centre, respectively. Stepwise NN solution of the first Centre had AUC 90.5±3.7% (CI: 83.8 to 95.1%). The Gini’s coefficients for LR and NN stepwise solutions of the first Centre were 0.712 and 0.816, respectively. When the LR and NN stepwise solutions were re-applied to the second Centre data, Gini’s coefficients were, respectively, 0.761 and 0.850. Few predictors were selected in common by LR and NN models: the presence of pre-operative shock, intubation and neurological symptoms, immediate post-operative presence of dialysis in continuous and the quantity of post-operative bleeding in the first 24 h. The length of extracorporeal circulation, post-operative chronic renal failure and the year of surgery were specifically detected by NN.Conclusions:Different from the International Registry of AAD, operative and immediate post-operative factors were seen as potential predictors of short-term mortality. We report a higher overall predictive accuracy with NN than with LR. However, the list of potential risk factors to predict 30-day mortality after AAD Type A by NN model is not enlarged significantly.

scholarly journals Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

2019 ◽  
Vol 9 (1) ◽  
pp. 171 ◽  
Author(s):  
Wei Chen ◽  
Zenghui Sun ◽  
Jichang Han
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Weights Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset

The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.

scholarly journals Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility

2020 ◽  
Vol 12 (20) ◽  
pp. 3389
Author(s):  
Alireza Arabameri ◽  
Ebrahim Karimi-Sangchini ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Indrajit Chowdhuri ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Human Life ◽  
Spatial Relationship ◽  
Model Performance ◽  
Area Under The Curve ◽  
Spatial Prediction ◽  
Susceptibility Maps ◽  
Soft Computing Techniques ◽  
Landslide Susceptibility Maps

Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of Alborz province, Iran, using hybrid Machine Learning (ML) algorithms, i.e., k-fold cross validation and ML techniques of credal decision tree (CDT), Alternative Decision Tree (ADTree), and their ensemble method (CDT-ADTree), which have been state-of-the-art soft computing techniques rarely used in the case of landslide susceptibility assessments. In this study, 22 key landslide causative factors (LCFs) were considered to explore their spatial relationship to landslides, based on local geomorphological and geo-environmental influences. The Random Forest (RF) algorithm was used for the identification of variables importance of different LCFs that are more prone to landslide susceptibility. A receiver operation characteristics (ROC) curve with area under the curve (AUC), accuracy, precision, and robustness index was used to evaluate and compare landslide susceptibility models. The output of the model performance shows that the CDT-ADTree model is the more robust model for the landslide susceptibility where the AUC, accuracy, and precision are 0.981, 0.837, and 0.867, respectively, than the standalone model of CDT and ADTree model. Therefore, it is concluded that the CDT-ADTree ensemble model can be applied as a new promising technique for spatial prediction of the landslide in further studies.

scholarly journals GIS-based spatial prediction of landslide susceptibility using logistic regression model

2011 ◽  
Vol 2 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Seyedeh Zohreh Mousavi ◽  
Ataollah Kavian ◽  
Karim Soleimani ◽  
Seyed Ramezan Mousavi ◽  
Ataollah Shirzadi
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Landslide Susceptibility ◽  
Logistic Regression Model ◽  
Spatial Prediction

scholarly journals Landslide susceptibility mapping on global scale using method of logistic regression

2016 ◽  
Author(s):  
Le Lin ◽  
Qigen Lin ◽  
Ying Wang
Keyword(s):  
Logistic Regression ◽  
Soil Moisture ◽  
Landslide Susceptibility ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Global Scale ◽  
Landslide Susceptibility Mapping ◽  
Landslide Occurrence ◽  
Explanatory Factors ◽  
Five Factors

Abstract. This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected to model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building model, 70 % of landslide and non-landslide points were randomly selected for logistic regression, and the others were used for model validation. For evaluating the accuracy of predictive models, this paper adopts several criteria including receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

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