Exploring the potential relationship between  the occurrence of debris flow and landslides

Abstract. The present study is to explore the potential relationship between debris flow and landslides by establishing susceptibility zoning maps (SZMs) separately with the use of random forest (RF). Lhünzê county, located in southeastern Tibet, was selected as the study area. The work was carried out with the following steps: (1) an inventory map consisting of 399 landslides and 49 debris flows was determined; (2) slope units and 11 conditioning factors were prepared for the susceptibility modeling of landslide while watershed units and 12 factors were prepared for debris flow; (3) SZMs were constructed for landslide and debris flow, respectively, with the use of RF; (4) the performance of two models was evaluated by 5-fold cross-validation using receiver operating characteristic (ROC), area under the curve (AUC) and statistical measures; (5) the potential relationship between landslide and debris flow was explored by the superimposition of two zoning maps; (6) the Gini index was applied to determine the major factors and analyze the difference between debris flow and landslides; (7) a combined susceptibility map with two considered hazardous phenomena was obtained. Two used models had demonstrated great predictive capabilities, with an accuracy of 87.33 % and 85.17 % and AUC of 0.902 and 0.892, respectively. Comparing the overlap of different susceptibility classes for two obtained maps, it was concluded that there is no straightforward relationship between the occurrence of debris flow and landslides. Although most landslides can be converted into debris flow, the area prone to debris flow did not promote the occurrence of a landslide. A susceptibility zoning map composed of two or more hazardous phenomena is comprehensive and significant in this regard, which provides a valuable reference for research studies of disaster-chain and engineering applications.

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Exploring the potential relationship between the occurrence of debris flow and landslide

10.5194/nhess-2020-294 ◽

2020 ◽

Author(s):

Zhu Liang ◽

Changming Wang ◽

Donghe Ma ◽

Kaleem Ullah Jan Khan

Keyword(s):

Random Forest ◽

Debris Flow ◽

Characteristic Curve ◽

Area Under The Curve ◽

Susceptibility Map ◽

Relative Operating Characteristic ◽

Conditioning Factors ◽

Statistical Measures ◽

Potential Relationship ◽

Major Factors

Abstract. he aim of the present study is to explore the potential relationship between debris flow and soil slide by establishing susceptibility zoning maps (SZM) separately with the use of random forest. Longzi County, located in Southeastern Tibet, where historical landslides occurred commonly, was selected as the study area. The work has been carried out with the following steps: (1) An inventory map consisting of 448 landslides (399 soil slides and 49 debris flows) was determined; (2) Slope units and 11 conditioning factors were prepared for the susceptibility modelling of landslide while watershed units and 12 factors for debris flow; (3) SZM were constructed for landslide and debris flow, respectively, with the use of random forest; (4) The performance of two models were evaluated by 5-fold cross-validation using relative operating characteristic curve (ROC), area under the curve (AUC) and statistical measures; (5) The potential relationship between soil slide and debris flow was explored by the superimposition of two zoning maps; (6) Gini index was applied to determined the major factors and analyze the difference between debris flow and soil slide; (7) A combined susceptibility map with two kinds of disaster was obtained. Two models had demonstrated great predictive capabilities, of which accuracy and AUC was 87.33 %, 0.902 and 85.17 %, 0.892, respectively. The loose sources need by the debris flow were not necessarily brought by the landslides although most landslides can be converted into debris flow. The area prone to debris flow did not promote the occurrence of landslide. A susceptibility zoning map composed of two or more natural disasters is comprehensive and significant in this regard, which provides valuable reference for researches of disaster-chain and engineering applications.

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A Global Debris Flow Susceptibility Map Based on a Comparative Analysis of a Single Global Model Versus a Continent-by-continent Approach

10.21203/rs.3.rs-1189090/v1 ◽

2021 ◽

Author(s):

Laurie Jayne Kurilla ◽

Giandomenico Fubelli

Keyword(s):

Comparative Analysis ◽

Debris Flow ◽

Flow Behavior ◽

Model Performance ◽

Global Model ◽

Susceptibility Map ◽

World Population ◽

Continental Scale ◽

Susceptibility Modeling ◽

Debris Flow Susceptibility

Abstract Debris flows, and landslides in general, are worldwide catastrophic phenomena. As world population and urbanization grow in magnitude and geographic coverage, the need exists to extend focus, research, and modeling to a continental and global scale.Although debris flow behavior and parameters are local phenomena, sound generalizations can be applied to debris flow susceptibility analyses at larger geographic extents based on these criteria. The focus of this research is to develop a global debris flow susceptibility map by modeling at both a continental scale for all continents and by a single global model and determine whether a global model adequately represents each continent. Probability Density, Conditional Probability, Certainty Factor, Frequency Ratio, and Maximum Entropy statistical models were developed and evaluated for best model performance using fourteen environmental factors generally accepted as the most appropriate debris flow predisposing factors. Global models and models for each continent were then developed and evaluated against verification data. The comparative analysis demonstrates that a single global model performs comparably or better than individual continental models for a majority of the continents, resulting in a debris flow susceptibility map of the world useful in international planning, and future debris flow susceptibility modeling for determining societal impacts.

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GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

Geoenvironmental Disasters ◽

10.1186/s40677-020-00166-8 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Ogbonnaya Igwe ◽

Ugwuoke Ikechukwu John ◽

Onwuka Solomon ◽

Ozioko Obinna

Keyword(s):

Soil Texture ◽

Slope Angle ◽

Area Under The Curve ◽

Gully Erosion ◽

Susceptibility Map ◽

Large Area ◽

Topographic Wetness Index ◽

Ahp Model ◽

Susceptibility Modeling ◽

Very High

AbstractGully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

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GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

10.21203/rs.3.rs-30110/v3 ◽

2020 ◽

Author(s):

Ogbonnaya Igwe ◽

Ikechukwu John Ugwuoke ◽

Onwuka Solomon ◽

Ozioko Obinna

Keyword(s):

Soil Texture ◽

Slope Angle ◽

Area Under The Curve ◽

Gully Erosion ◽

Susceptibility Map ◽

Large Area ◽

Topographic Wetness Index ◽

Ahp Model ◽

Susceptibility Modeling ◽

Very High

Abstract Gully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

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Deep Learning for Predicting Cognitive Gap as a Reliable Biomarker of Dementia

10.1101/2021.01.24.21249625 ◽

2021 ◽

Author(s):

Tetiana Habuza ◽

Nazar Zaki ◽

Yauhen Statsenko ◽

Fady Alnajjar ◽

Sanaa Elyassami

Keyword(s):

Area Under The Curve ◽

Human Cognition ◽

Functional Abilities ◽

Brain Images ◽

Proposed Model ◽

Neuroimaging Data ◽

Healthy Control ◽

The Difference ◽

Roc Area ◽

The Relationship

AbstractNeuroimaging data may reflect the mental status of both cognitively preserved individuals and patients with neurodegenerative diseases. To find the relationship between cognitive performance and the difference between predicted and observed functional test results, we developed a Convolutional Neural Network (CNN) based regression model to estimate the level of cognitive decline from preprocessed T1-weighted MRI images. In this study, we considered the Predicted Cognitive Gap (PCG) as the biomarker to accurately classify Healthy Control (HC) subjects versus Alzheimer disease (AD) subjects. The proposed model was tested on a dataset that includes 422 HC and 377 AD cases. The performance of the proposed solution was measured using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and achieved 0.987 (ADAS-cog), 0.978 (MMSE), 0.898 (RAVLT), 0.848 (TMT), 0.829 (DSST) for averaged brain images; and 0.985 (ADAS-cog), 0.987 (MMSE), 0.901 (RAVLT), 0.8474 (TMT), 0.796 (DSST) for middle slice skull stripped brain images. The results achieved indicate that PCG can accurately separate healthy subjects from demented ones and thus, the structure of the brain contributes to the level of human cognition and their functional abilities. Therefore, PCG could be used as a biomarker for dementia.

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GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques

Applied Sciences ◽

10.3390/app10010016 ◽

2019 ◽

Vol 10 (1) ◽

pp. 16 ◽

Cited By ~ 28

Author(s):

Xia Zhao ◽

Wei Chen

Keyword(s):

Landslide Susceptibility ◽

Area Under The Curve ◽

Training Data ◽

Statistical Parameters ◽

Validation Data ◽

Rotation Forest ◽

Conditioning Factors ◽

Susceptibility Modeling ◽

Ensemble Techniques ◽

Landslide Inventory Map

The main purpose of this paper is to use ensembles techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), dagging-functional trees (DFT)) for landslide susceptibility modeling in Zichang County, China. Firstly, 263 landslides were identified, and the landslide inventory map was established, and the landslide locations were randomly divided into 70% (training data) and 30% (validation data). Then, 14 landslide conditioning factors were selected. Furthermore, the correlation analysis between conditioning factors and landslides was applied using the certainty factor method. Hereafter, four models were applied for landslide susceptibility modeling and zoning. Finally, the receiver operating characteristic (ROC) curve and statistical parameters were used to evaluate and compare the overall performance of the four models. The results showed that the area under the curve (AUC) for the four models was larger than 0.74. Among them, the BFT model is better than the other three models. In addition, this study also illustrated that the integrated model is not necessarily more effective than a single model. The ensemble data mining technology used in this study can be used as an effective tool for future land planning and monitoring.

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A Novel Rule-Based Approach in Mapping Landslide Susceptibility

Sensors ◽

10.3390/s19102274 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2274 ◽

Cited By ~ 10

Author(s):

Majid Roodposhti ◽

Jagannath Aryal ◽

Biswajeet Pradhan

Keyword(s):

Landslide Susceptibility ◽

Area Under The Curve ◽

Local Scale ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Operating Characteristics ◽

Rule Based ◽

Conditioning Factors ◽

Rule Sets ◽

Rule Based Approach

Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.

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On the Major Differences between the Banking Systems of the EAEU States

Economics and Management ◽

10.35854/1998-1627-2020-4-397-406 ◽

2020 ◽

Vol 26 (4) ◽

pp. 397-406

Author(s):

T. E. Chekanova

Keyword(s):

Financial Stability ◽

Systems Approach ◽

Negative Impact ◽

Banking Regulation ◽

Member States ◽

Banking Systems ◽

Significant Negative Impact ◽

The Difference ◽

The Government ◽

Major Factors

The presented study examines the problems of integration of the national banking systems of the member states of the Eurasian Economic Union (EAEU).Aim. The study aims to examine the major differences in various aspects of functioning of banking systems in the EAEU member states in terms of their impact on integration processes.Tasks. The author identifies the most prominent features of the banking systems of the EAEU states; reveals the depth of the existing differences through a comparative analysis of various indicators of national banking systems; outlines ways of overcoming integration problems associated with differences in the banking sectors of the Union states.Methods. This study is based on universal general scientific methods and elements of comparative, functional, and economic analysis within the framework of a systems approach. The author uses regulatory documents and banking reports of the EAEU states, statistical and analytical materials of the Eurasian Economic Commission (EEC), and data of Moody’s international rating agency.Results. The study identifies a number of aspects that contain the major differences in the functioning of banking systems in the EAEU member states; highlights the disproportions in the scale, level of development, financial stability, and risks of the banking spheres of the Union states; comparatively analyzes the proportion of banking and non-banking structures in the system and the share of the government and non-resident companies in the capital of banks; marks the difference in the pricing of banking services; determines differences in the existing approaches to banking regulation and the established standards; analyzes the major differences in the legislative acts of the central banks and governments of the EAEU member states and in the terms and definitions used. According to the results of the study, the major factors hindering the development of integration processes between the banking systems of the EAEU states are identified.Conclusions. The existing differences between the banking systems of the EAEU countries are diverse and multifaceted. The author states that the aspects addressed in this study have a significant negative impact on the further development of integration processes, describing the major directions and actions of the member states aimed at minimizing the exiting differences, which are required to facilitate the convergence of the states and the transition towards a common financial market.

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Development and Validation of Spectrophotometric and Liquid Chromatographic Methods for Estimation of Tigecycline in Injections

International Journal of Pharmaceutical Sciences and Nanotechnology ◽

10.37285/ijpsn.2020.13.5.12 ◽

2020 ◽

Vol 13 (5) ◽

pp. 5148-5154

Author(s):

Sagar Suman Panda ◽

Ravi Kumar B.V.V.

Keyword(s):

Area Under The Curve ◽

Reversed Phase ◽

Equal Concentration ◽

Pharmaceutical Dosage Form ◽

Liquid Chromatographic Method ◽

Array Detection ◽

The Difference ◽

The One ◽

Development And Validation ◽

Liquid Chromatographic

Three new analytical methods were optimized and validated for the estimation of tigecycline (TGN) in its injection formulation. A difference UV spectroscopic, an area under the curve (AUC), and an ultrafast liquid chromatographic (UFLC) method were optimized for this purpose. The difference spectrophotometric method relied on the measurement of amplitude when equal concentration solutions of TGN in HCl are scanned against TGN in NaOH as reference. The measurements were done at 340 nm (maxima) and 410nm (minima). Further, the AUC under both the maxima and minima were measured at 335-345nm and 405-415nm, respectively. The liquid chromatographic method utilized a reversed-phase column (150mm×4.6mm, 5µm) with a mobile phase of methanol: 0.01M KH2PO4 buffer pH 3.5 (using orthophosphoric acid) in the ratio 80:20 %, v/v. The flow rate was 1.0ml/min, and diode array detection was done at 349nm. TGN eluted at 1.656min. All the methods were validated for linearity, precision, accuracy, stability, and robustness. The developed methods produced validation results within the satisfactory limits of ICH guidance. Further, these methods were applied to estimate the amount of TGN present in commercial lyophilized injection formulations, and the results were compared using the One-Way ANOVA test. Overall, the methods are rapid, simple, and reliable for routine quality control of TGN in the bulk and pharmaceutical dosage form.

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Integration of an InSAR and ANN for Sinkhole Susceptibility Mapping: A Case Study from Kirikkale-Delice (Turkey)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030119 ◽

2021 ◽

Vol 10 (3) ◽

pp. 119

Author(s):

Hakan A. Nefeslioglu ◽

Beste Tavus ◽

Melahat Er ◽

Gamze Ertugrul ◽

Aybuke Ozdemir ◽

...

Keyword(s):

High Speed ◽

Fundamental Problem ◽

Vertical Displacement ◽

Deformation Monitoring ◽

Susceptibility Mapping ◽

Susceptibility Map ◽

Engineering Structures ◽

Conditioning Factors ◽

High Deformation ◽

Settlement Site

Suitable route determination for linear engineering structures is a fundamental problem in engineering geology. Rapid evaluation of alternative routes is essential, and novel approaches are indispensable. This study aims to integrate various InSAR (Interferometric Synthetic Aperture Radar) techniques for sinkhole susceptibility mapping in the Kirikkale-Delice Region of Turkey, in which sinkhole formations have been observed in evaporitic units and a high-speed train railway route has been planned. Nine months (2019–2020) of ground deformations were determined using data from the European Space Agency’s (ESA) Sentinel-1A/1B satellites. A sinkhole inventory was prepared manually using satellite optical imagery and employed in an ANN (Artificial Neural Network) model with topographic conditioning factors derived from InSAR digital elevation models (DEMs) and morphological lineaments. The results indicate that high deformation areas on the vertical displacement map and sinkhole-prone areas on the sinkhole susceptibility map (SSM) almost coincide. InSAR techniques are useful for long-term deformation monitoring and can be successfully associated in sinkhole susceptibility mapping using an ANN. Continuous monitoring is recommended for existing sinkholes and highly susceptible areas, and SSMs should be updated with new results. Up-to-date SSMs are crucial for the route selection, planning, and construction of important transportation elements, as well as settlement site selection, in such regions.

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