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>

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

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.

scholarly journals LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

2020 ◽  
Vol 21 (4) ◽  
pp. 1508
Author(s):  
Yi Zhang ◽  
Min Chen ◽  
Ang Li ◽  
Xiaohui Cheng ◽  
Hong Jin ◽  
...  
Keyword(s):  
Computational Methods ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Weighted Networks ◽  
Disease Associations ◽  
Space Projection ◽  
Auc Value ◽  
Leave One Out ◽  
Key Steps

Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA–disease associations. In this research, we proposed a lncRNA–disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA–disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA–disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA–disease associations and isolated diseases.

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 The Value of Predicting Human Epidermal Growth Factor Receptor 2 Status in Adenocarcinoma of the Esophagogastric Junction on CT-Based Radiomics Nomogram

2021 ◽  
Vol 11 ◽  
Author(s):  
Shuxing Wang ◽  
Yiqing Chen ◽  
Han Zhang ◽  
Zhiping Liang ◽  
Jun Bu
Keyword(s):  
Logistic Regression ◽  
Esophagogastric Junction ◽  
Area Under The Curve ◽  
Growth Factor Receptor ◽  
Predictive Performance ◽  
Regression Method ◽  
Training Cohort ◽  
T Stage ◽  
Logistic Regression Method ◽  
Radiomics Signature

PurposeWe developed and validated a CT-based radiomics nomogram to predict HER2 status in patients with adenocarcinoma of esophagogastric junction (AEG).MethodA total of 101 patients with HER2-positive (n=46) and HER2-negative (n=55) esophagogastric junction adenocarcinoma (AEG) were retrospectively analyzed. They were then randomly divided into a training cohort (n=70) and a verification cohort (n=31). The radiomics features were obtained from the portal phase of the CT enhanced scan. We used the least absolute shrinkage and selection operator (LASSO) logistic regression method to select the best radiomics features in the training cohort, combined them linearly, and used the radiomics signature formula to calculate the radiomics score (Rad-score) of each AEG patient. A multivariable logistic regression method was applied to develop a prediction model that incorporated the radiomics signature and independent risk predictors. The prediction performance of the nomogram was evaluated using the training and validation cohorts.ResultIn the training (P<0.001) and verification groups (P<0.001), the radiomics signature combined with seven radiomics features was significantly correlated with HER2 status. The nomogram composed of CT-reported T stage and radiomics signature showed very good predictive performance for HER2 status. The area under the curve (AUC) of the training cohort was 0.946 (95% CI: 0.919–0.973), and that of the validation group was 0.903 (95% CI: 0.847–0.959). The calibration curve of the radiomics nomogram showed a good degree of calibration. Decision-curve analysis revealed that the radiomics nomogram was useful.ConclusionThe nomogram CT-based radiomics signature combined with CT-reported T stage can better predict the HER2 status of AEG before surgery. It can be used as a non-invasive prediction tool for HER2 status and is expected to guide clinical treatment decisions in clinical practice, and it can assist in the formulation of individualized treatment plans.

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 Potential Characteristics for Prediction of the Efficacy of Non-Steroid Anti-Inflammatory Drugs for Migraine Treatment: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Heng-Le Wei ◽  
Jin-Jin Wang ◽  
Gang-Ping Zhou ◽  
Yu-Sheng Yu ◽  
Hong Zhang
Keyword(s):  
Logistic Regression ◽  
Area Under The Curve ◽  
Analgesic Efficacy ◽  
Considerable Proportion ◽  
Migraine Treatment ◽  
Inflammatory Drugs ◽  
Auc Value ◽  
Anti Inflammatory ◽  
Headache Impact ◽  
Prediction Capability

Abstract Background: Although several special medications for migraine are currently available, non-steroid anti-inflammatory drugs (NSAIDs) are still the first-line pharmacological option. A considerable proportion of migraineurs still be unresponsive to NSAIDs for reasons that remain unknown. The study aimed to develop significant characteristics to predict the efficacy of NSAIDs for patients with migraine. Methods: In this retrospective study, 567 patients suffering migraine were included and divided into an effective NSAIDs (M-eNSAIDs) group and a noneffective NSAIDs (M-neNSAIDs) group according to the analgesic efficacy at 2 hours after taking NSAIDs. Clinical and neuropsychiatric characteristics were collected and used to build a logistic regression model. And a receiver operating characteristic (ROC) curve was drawn to represent the prediction capability.Results: Five predictors including education, attack duration, headache impact intensity, anxiety and depression scores were identified to build the logistic regression modal. The area under the curve (AUC) values of each predictor were 0.706, 0.639, 0.560, 0.683 and 0.632, respectively, failing to predict the efficacy of NSAIDs. All five predictors-combined method achieved an acceptable AUC value of 0.834 and a sensitivity of 90.9%.Conclusions: Despite the insufficient predictive capability of these predictors when analyzed individually, this study developed an effective and convenient method to accurately predict the efficacy of NSAIDs, which would be helpful for developing individualized therapeutic strategies for treatment of migraine.

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.

Preoperative Circulating MiR-210, a Risk Factor for Postoperative Delirium Among Elderly Patients with Gastric Cancer Undergoing Curative Resection

2020 ◽  
Vol 26 (40) ◽  
pp. 5213-5219
Author(s):  
Yun Chen ◽  
Jinwei Zheng ◽  
Junping Chen
Keyword(s):  
Gastric Cancer ◽  
Logistic Regression ◽  
Risk Factor ◽  
Elderly Patients ◽  
Curative Resection ◽  
Postoperative Delirium ◽  
Area Under The Curve ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Clinicopathologic Characteristics

Background: Postoperative delirium (POD) is a very common complication in elderly patients with gastric cancer (GC) and associated with poor prognosis. MicroRNAs (miRNAs) serve as key post-transcriptional regulators of gene expression via targeting mRNAs and play important roles in the nervous system. This study aimed to investigate the potential predictive role of miRNAs for POD. Methods: Elderly GC patients who were scheduled to undergo elective curative resection were consequently enrolled in this study. POD was assessed at 1 day before surgery and 1-7 days after surgery following the guidance of the 5th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM V, 2013). The demographics, clinicopathologic characteristics and preoperative circulating miRNAs by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were compared between patients with or without POD. Risk factors for POD were assessed via univariate and multivariate logistic regression analyses. Results: A total of 370 participants were enrolled, of which 63 had suffered from POD within postoperative 7 days with an incidence of 17.0%. Preoperative miR-210 was a predictor for POD with an area under the curve (AUC) of 0.921, a cut-off value of 1.67, a sensitivity of 95.11%, and a specificity of 92.06%, (P<0.001). In the multivariate logistic regression model, the relative expression of serum miR-210 was an independent risk factor for POD (OR: 3.37, 95%CI: 1.98–5.87, P=0.003). Conclusions: In conclusion, the present study highlighted that preoperative miR-210 could serve as a potential predictor for POD in elderly GC patients undergoing curative resection.

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