GIS-based Landslide Susceptibility Mapping Using Logistic Regression Analysis: A Case study in the Kadernath valley, Central Himalaya.

2020 ◽  
Author(s):  
Suman Das
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Sensor Data ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Anthropogenic Factors ◽  
Susceptibility Map

<p>Himalayan Terrain is highly susceptible to landslide events triggered by frequent earthquakes and heavy rainfall. In the recent past, cloud burst events are on rising, causing massive loss of life and property, mainly attributed to climate change and extensive anthropogenic activities in the mountain region as experienced in case of 2013 Kedarnath Tragedy. The study aimed to identify the potential landslide hazard zone in Mandakini valley by utilizing different types of data including Survey of India toposheet, geological (lithological and structural) maps, IRS-1D, LISS IV multispectral and PAN satellite sensor data and field observations. Relevant 18 thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, LULC, NDVI, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques.  A detailed landslide susceptibility map was produced using a logistic regression method with datasets developed in GIS. which has further been categorized into four landslide susceptibility zones from high to very low. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. ROC curve analysis showing an accuracy of 87.3 % for an independent set of test samples. The result also showed a strong agreement between the distribution of existing landslides and predicted landslide susceptibility zones. Consequently, this study could serve as an effective guide for further land-use planning and for the implementation of development.</p>

scholarly journals Landslide Susceptibility Mapping Using Logistic Regression Analysis along the Jinsha River and Its Tributaries Close to Derong and Deqin County, Southwestern China

2018 ◽  
Vol 7 (11) ◽  
pp. 438 ◽  
Author(s):  
Xiaohui Sun ◽  
Jianping Chen ◽  
Yiding Bao ◽  
Xudong Han ◽  
Jiewei Zhan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Influencing Factors ◽  
Logistic Regression Analysis ◽  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Key Factors ◽  
Jinsha River ◽  
Landslide Occurrence

The objective of this study was to identify the areas that are most susceptible to landslide occurrence, and to find the key factors associated with landslides along Jinsha River and its tributaries close to Derong and Deqin County. Thirteen influencing factors, including (a) lithology, (b) slope angle, (c) slope aspect, (d) TWI, (e) curvature, (f) SPI, (g) STI, (h) topographic relief, (i) rainfall, (j) vegetation, (k) NDVI, (l) distance-to-river, (m) and distance-to-fault, were selected as the landslide conditioning factors in landslide susceptibility mapping. These factors were mainly obtained from the field survey, digital elevation model (DEM), and Landsat 4–5 imagery using ArcGIS software. A total of 40 landslides were identified in the study area from field survey and aerial photos’ interpretation. First, the frequency ratio (FR) method was used to clarify the relationship between the landslide occurrence and the influencing factors. Then, the principal component analysis (PCA) was used to eliminate multiple collinearities between the 13 influencing factors and to reduce the dimension of the influencing factors. Subsequently, the factors that were reselected using the PCA were introduced into the logistic regression analysis to produce the landslide susceptibility map. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. The landslide susceptibility map was divided into the following five classes: very low, low, moderate, high, and very high. The results showed that the ratios of the areas of the five susceptibility classes were 23.14%, 22.49%, 18.00%, 19.08%, and 17.28%, respectively. And the prediction accuracy of the model was 83.4%. The results were also compared with the FR method (79.9%) and the AHP method (76.9%), which meant that the susceptibility model was reasonable. Finally, the key factors of the landslide occurrence were determined based on the above results. Consequently, this study could serve as an effective guide for further land use planning and for the implementation of development.

Landslide Susceptibility Mapping in the Commune of Oudka, Taounate Province, North Morocco: A Comparative Analysis of Logistic Regression, Multivariate Adaptive Regression Spline, and Artificial Neural Network Models

2020 ◽  
Vol 26 (2) ◽  
pp. 185-200
Author(s):  
Said Benchelha ◽  
Hasnaa Chennaoui Aoudjehane ◽  
Mustapha Hakdaoui ◽  
Rachid El Hamdouni ◽  
Hamou Mansouri ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Multivariate Adaptive Regression Spline ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Neural Network Models ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Artificial Neural

ABSTRACT Landslide susceptibility indices were calculated and landslide susceptibility maps were generated for the Oudka, Morocco, study area using a geographic information system. The spatial database included current landslide location, topography, soil, hydrology, and lithology, and the eight factors related to landslides (elevation, slope, aspect, distance to streams, distance to roads, distance to faults, lithology, and Normalized Difference Vegetation Index [NDVI]) were calculated or extracted. Logistic regression (LR), multivariate adaptive regression spline (MARSpline), and Artificial Neural Networks (ANN) were the methods used in this study to generate landslide susceptibility indices. Before the calculation, the study area was randomly divided into two parts, the first for the establishment of the model and the second for its validation. The results of the landslide susceptibility analysis were verified using success and prediction rates. The MARSpline model gave a higher success rate (AUC (Area Under The Curve) = 0.963) and prediction rate (AUC = 0.951) than the LR model (AUC = 0.918 and AUC = 0.901) and the ANN model (AUC = 0.886 and AUC = 0.877). These results indicate that the MARSpline model is the best model for determining landslide susceptibility in the study area.

scholarly journals A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

2021 ◽  
Vol 10 (2) ◽  
pp. 93
Author(s):  
Wei Xie ◽  
Xiaoshuang Li ◽  
Wenbin Jian ◽  
Yang Yang ◽  
Hongwei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Assessment Model ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Area Index

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

scholarly journals Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India

2021 ◽  
Vol 30 (4) ◽  
pp. 683-691
Author(s):  
G. Kavitha ◽  
S. Anbazhagan ◽  
S. Mani
Keyword(s):  
Landslide Susceptibility ◽  
Drainage Density ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Landslide Susceptibility Zonation ◽  
Road Section ◽  
Gps Measurement ◽  
Causative Factors ◽  
Very High

Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.

O-207 Which infertile patients mostly deserve to have a sperm DNA fragmentation index done? Findings from a cross-sectional study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
E Pozzi ◽  
L Boeri ◽  
L Candela ◽  
D Cignoli ◽  
G Colandrea ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Dna Fragmentation ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Sperm Concentration ◽  
Testicular Volume ◽  
Sperm Dna Fragmentation ◽  
Sperm Dna ◽  
Increased Risk

Abstract Study question Current scientific guidelines do not clearly suggest which patients would benefit the most from a sperm DNA fragmentation (SDF) test. Summary answer We aimed to investigate potential predictive factors for altered SDF in a homogenous cohort of white-European men presenting for primary couple’s infertility. What is known already High SDF has been associated with reduced fertilization rates, reduced chances of natural conception and an increased risk of early pregnancy loss. Study design, size, duration Data from 478 consecutive men with normal or altered SDF were analysed. Infertility was defined according to the WHO criteria. Semen analysis, SDF (according to SCSA) and serum hormones were measured in every patient. Health significant comorbidities were scored with the Charlson Comorbidity Index (CCI). Altered SDF was considered with a threshold of > 30%. Participants/materials, setting, methods Descriptive statistics compared the overall characteristics of patients with normal SDF and altered SDF. Logistic regression analysis tested potential predictors of altered SDF. ROC curve was used to test the accuracy of the model in predicting SDF alteration Main results and the role of chance Of 478 patients, 253 (57.7%) had altered SDF. Median (IQR) age and BMI of the whole cohort were 38 (35-42) years and 25.1 (23.3-27.1) kg/m2 respectively. Patients with altered SDF were older (median (IQR) age: 39 (36-43) vs. 37 (34-38) years, p < 0.0001), had lower sperm concentration (5 (1.1–18) vs. 17 x106/mL (6–38.8), p < 0.0001), testicular volume (15.1 (12 –20) vs. 16.8 (12 – 25) Prader, p = 0.0005), and total motile sperm count (TMSC) (1.8 (0.21–10.71) vs. 11.8x106 (2–37.26), p < 0.0001). Conversely, men with altered SDF had higher FSH (6.1 (3.85–9.7) vs. 4.8 (3.85 – 7.9) mIU/mL, p < 0.0001) and prolactin levels (9.8 (7.43–14.04) vs. 8.3 (6.6–11.3) pg/mL, p = 0.0004) than those with normal SDF. At multivariable logistic regression analysis, patients’ age >35 years (OR: 2.45, p = 0.0009), FSH > 8.0 mIU/mL (OR: 2.23, p < 0.0001) and lower TMSC (OR: 2.04, p = 0.002) were identified as indipendent predictors of altered SDF, after adjusting for testicular volume and CCI≥1. ROC curve (Figure 1) revealed that the model has a good predictive ability to identify patients with SDF alteration (AUC: 0.72, 95%CI: 0.67 - 0.77). Limitations, reasons for caution It is a retrospective analysis at a single, tertiary-referral academic centre, thus raising the possibility of selection biases. In spite of this, all patients have been consistently analysed over time with a rigorous follow-up, thus limiting potential heterogeneity in terms of data reporting Wider implications of the findings Primary infertile men older than 35 years, with high serum FSH and low TMSC at baseline are the ones who mostly deserve a SDF test over their diagnostic work-up and that would potentially benefit the most of certain treatments to improve SDF value, thus increasing chances of conceiving. Trial registration number Not applicable

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

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