scholarly journals Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches

2021 ◽  
Vol 13 (24) ◽  
pp. 5063
Author(s):  
Jiyuan Hu ◽  
Mahdi Motagh ◽  
Jiayao Wang ◽  
Fen Qin ◽  
Jianchen Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
High Risk ◽  
Ground Subsidence ◽  
Karst Area ◽  
Angular Distortion ◽  
Karst Collapse ◽  
Risk Zoning ◽  
Risk Zonation ◽  
Risk Zones ◽  
Hierarchy Process

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

scholarly journals Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: a case study in the mountainous terrain of Northeast India

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ridalin Lamat ◽  
Mukesh Kumar ◽  
Arnab Kundu ◽  
Deepak Lal
Keyword(s):  
High Risk ◽  
Forest Fire ◽  
Analytical Hierarchy Process ◽  
Fire Risk ◽  
Control Measures ◽  
Fire Control ◽  
Risk Zones ◽  
Hierarchy Process ◽  
Forest Fire Risk ◽  
Very High

AbstractThis study presents a geospatial approach in conjunction with a multi-criteria decision-making (MCDM) tool for mapping forest fire risk zones in the district of Ri-Bhoi, Meghalaya, India which is very rich in biodiversity. Analytical hierarchy process (AHP)-based pair-wise comparison matrix was constructed to compare the selected parameters against each other based on their impact/influence (equal, moderate, strong, very strong, and extremely strong) on a forest fire. The final output delineated fire risk zones in the study area in four categories that include very high-risk, high-risk, moderate-risk, and low-risk zones. The delineated fire risk zones were found to be in close agreement with actual fire points obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) fire data for the study area. Results indicated that Ri-Bhoi’s 804.31 sq. km. (32.86%) the area was under ‘very high’ fire susceptibility. This was followed by 583.10 sq. km. (23.82%), 670.47 sq. km. (27.39%), and 390.12 sq. km. (15.93%) the area under high, moderate, and low fire risk categories, respectively. These results can be used effectively to plan fire control measures in advance and the methodology suggested in this study can be adopted in other areas too for delineating potential fire risk zones.

scholarly journals COMPARISON OF ANALYTICAL HIERARCHY PROCESS AND FUZZY METHOD IN DEFORESTATION RISK ZONING

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 851-856
Author(s):  
M. Pir Bavaghar ◽  
H. Ghazanfari ◽  
S. Rahimi
Keyword(s):  
Combination Method ◽  
Linguistic Terms ◽  
Resources Management ◽  
Risk Zoning ◽  
Fuzzy Methods ◽  
Special Weight ◽  
Risk Zones ◽  
Gis Environment ◽  
Hierarchy Process ◽  
Very High

Abstract. Detection and prediction of land-cover changes are powerful tools in natural resources management and ecosystem assessment. This study was carried out to compare multi-criteria decision techniques (AHP and fuzzy) in deforestation risk zoning. The TM images of Landsat 5 were used to produce deforestation map during 1989 to 2011. In the next step, the most important criteria affecting deforestation were determined. The final weights of criteria were computed using expert's judgments, pairwise comparisons by AHP and also linguistic terms by fuzzy technique. Weighted linear combination method was used to combining the criteria, and each of the generated maps with its special weight was integrated into the GIS environment. The final deforestation risk zoning map, in both methods of AHP and fuzzy, were classified into five classes including of very high, high, moderate, low and very low risk.Evaluation of the results showed that 81.07 and 80.65 percentages of deforestation are located in the very high and high risk zones in the maps derived from AHP and fuzzy approaches, respectively. Based on the results, AHP and fuzzy methods have suitable performance in deforestation risk zoning. Thus, despite the different nature of the AHP and fuzzy methods, it was observed that these two methods do not have much difference in deforestation risk zoning of the study area, in practice.

scholarly journals Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1650
Author(s):  
Hassan Waqas ◽  
Linlin Lu ◽  
Aqil Tariq ◽  
Qingting Li ◽  
Muhammad Fahad Baqa ◽  
...  
Keyword(s):  
High Risk ◽  
Frequency Ratio ◽  
Flash Flood ◽  
Drainage Density ◽  
Low Risk ◽  
Physical Parameters ◽  
Observation Data ◽  
Risk Zones ◽  
Hierarchy Process ◽  
Very High

Pakistan is a flood-prone country and almost every year, it is hit by floods of varying magnitudes. This study was conducted to generate a flash flood map using analytical hierarchy process (AHP) and frequency ratio (FR) models in the ArcGIS 10.6 environment. Eight flash-flood-causing physical parameters were considered for this study. Five parameters were based on the digital elevation model (DEM), Advanced Land Observation Satellite (ALOS), and Sentinel-2 satellite, including distance from the river and drainage density slope, elevation, and land cover, respectively. Two other parameters were geology and soil, consisting of different rock and soil formations, respectively, where both layers were classified based on their resistance against water percolation. One parameter was rainfall. Rainfall observation data obtained from five meteorological stations exist close to the Chitral District, Pakistan. According to its significant importance in the occurrence of a flash flood, each criterion was allotted an estimated weight with the help of AHP and FR. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of the drainage density was given the highest value. This gave the output in terms of five flood risk zones: very high risk, high risk, moderate risk, low risk, and very low risk. According to the results, 1168 km2, that is, 8% of the total area, showed a very high risk of flood occurrence. Reshun, Mastuj, Booni, Colony, and some other villages were identified as high-risk zones of the study area, which have been drastically damaged many times by flash floods. This study is pioneering in its field and provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists.

scholarly journals Multidimensional dynamic healthcare personnel (HCP)-centric model from a low-income and middle-income country to support and protect COVID-19 warriors: a large prospective cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043837
Author(s):  
Usha Dutta ◽  
Anurag Sachan ◽  
Madhumita Premkumar ◽  
Tulika Gupta ◽  
Swapnajeet Sahoo ◽  
...  
Keyword(s):  
High Risk ◽  
Low Income ◽  
Tertiary Care ◽  
Low Risk ◽  
Transmission Risk ◽  
Healthcare Personnel ◽  
Risk Zone ◽  
Increased Risk ◽  
Medium Risk ◽  
Risk Zones

ObjectivesHealthcare personnel (HCP) are at an increased risk of acquiring COVID-19 infection especially in resource-restricted healthcare settings, and return to homes unfit for self-isolation, making them apprehensive about COVID-19 duty and transmission risk to their families. We aimed at implementing a novel multidimensional HCP-centric evidence-based, dynamic policy with the objectives to reduce risk of HCP infection, ensure welfare and safety of the HCP and to improve willingness to accept and return to duty.SettingOur tertiary care university hospital, with 12 600 HCP, was divided into high-risk, medium-risk and low-risk zones. In the high-risk and medium-risk zones, we organised training, logistic support, postduty HCP welfare and collected feedback, and sent them home after they tested negative for COVID-19. We supervised use of appropriate personal protective equipment (PPE) and kept communication paperless.ParticipantsWe recruited willing low-risk HCP, aged <50 years, with no comorbidities to work in COVID-19 zones. Social distancing, hand hygiene and universal masking were advocated in the low-risk zone.ResultsBetween 31 March and 20 July 2020, we clinically screened 5553 outpatients, of whom 3012 (54.2%) were COVID-19 suspects managed in the medium-risk zone. Among them, 346 (11.4%) tested COVID-19 positive (57.2% male) and were managed in the high-risk zone with 19 (5.4%) deaths. One (0.08%) of the 1224 HCP in high-risk zone, 6 (0.62%) of 960 HCP in medium-risk zone and 23 (0.18%) of the 12 600 HCP in the low-risk zone tested positive at the end of shift. All the 30 COVID-19-positive HCP have since recovered. This HCP-centric policy resulted in low transmission rates (<1%), ensured satisfaction with training (92%), PPE (90.8%), medical and psychosocial support (79%) and improved acceptance of COVID-19 duty with 54.7% volunteering for re-deployment.ConclusionA multidimensional HCP-centric policy was effective in ensuring safety, satisfaction and welfare of HCP in a resource-poor setting and resulted in a willing workforce to fight the pandemic.

Development of a breast cancer-specific prognostic tool using CancerLinQ Discovery.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 275-275
Author(s):  
Emily Miller Ray ◽  
Xinyi Zhang ◽  
Lisette Dunham ◽  
Xianming Tan ◽  
Jennifer Elston Lafata ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Metastatic Breast Cancer ◽  
High Risk ◽  
Performance Status ◽  
Vital Signs ◽  
Metastatic Breast ◽  
Clinical Use ◽  
Final Model ◽  
Prognostic Tool

275 Background: Oncologists often struggle to know which patients are near end of life to enable a timely transition to supportive care. We developed a breast cancer-specific prognostic tool, using electronic health record data from CancerLinQ Discovery (CLQD), to help identify patients at high risk of near-term death. We created multiple candidate models with varying thresholds for defining high risk that will be considered for future clinical use. Methods: We included patients with breast cancer diagnosed between 1/1/2000 to 6/1/2020 who had at least one encounter with vital signs and evidence of metastatic breast cancer (MBC). All encounters from 1/1/2000 to 7/5/2020 were included. We used multiple imputation (MI) to impute missing numeric variables and treated missing values as a new level for categorical variables. We sampled one encounter per patient and oversampled within 30 days of death, so that the event rate (death within 30 days of encounter) was about 10%. We randomly divided these patients into training (70%) and test datasets (30%). We evaluated candidate predictors of the event using logistic regression with forward variable selection. Candidate predictors included age, vital signs, laboratory values, performance status, pain score, time since chemotherapy, and ER/PR/HER2 receptor status, and change from baseline and change rate of numeric variables. We obtained a single final model by combining resulted logistic regression model from 10 MI training sets. We evaluated this final model on the MI test sets. We varied the alert threshold (i.e., high-risk proportion) from 5% to 40%. Results: We identified 9,270 patients, representing 586,801 encounters. Significant predictors of mortality were: increased age, decreased age at diagnosis, negative change in body mass index, low albumin, high ALP, high AST, high WBC, low sodium, high creatinine, worse performance status, low pulse oximetry, increased age with increased creatinine, high pain score with no opiates, increased pulse rate, unknown/missing PR, opiate use in past 3 months, and prior chemotherapy in past 1 year but not past 30 days. Candidate models had prediction accuracy of 70-89% and positive predictive value of 31-77%. Conclusions: Demographic and clinical variables can be used to predict risk of death within 30 days of a clinical encounter for patients with MBC. Next steps include selection of a preferred model for clinical use, balancing performance characteristics and acceptability, followed by implementation and evaluation of the prognostic tool in the clinic. Candidate models, varying by threshold or percentage of patients assumed to be at high risk, for the outcome of death within 30 days among patients with metastatic breast cancer.[Table: see text]

scholarly journals Optimizing Reduction of Groundwater Discharge to Control Land Subsidence (Case study: Nadjafabad Aquifer)

2021 ◽  
Author(s):  
hamid Kardan moghaddam ◽  
Zahra Rahimzadeh kivi ◽  
Fatemeh Javadi ◽  
Mohammad Heydari
Keyword(s):  
Land Subsidence ◽  
Groundwater Resources ◽  
Treatment Strategies ◽  
Flow Simulation ◽  
Maximum Amount ◽  
Water Withdrawal ◽  
Ground Subsidence ◽  
Risk Zoning ◽  
Dry Conditions ◽  
The Impact

Abstract This study evaluates and predicts the ground subsidence that happens due to the haphazard operation of groundwater resources. Also, several strategies have been developed to control this unpleasant phenomenon. For this purpose, groundwater flow simulation has been conducted using MODFLOW numerical model, and subsidence simulation in Najafabad plain has been done using SUB package under three climatic scenarios for future periods. Examination of the simulation results shows that the amount of land subsidence will increase with the aquifer operation's continuation. The maximum amount of subsidence for 6 years in drought conditions will be 23 cm at the aquifer's outlet. According to the land subsidence results at the aquifer, risk zoning of the aquifer operation was done to develop a solution to reduce the withdrawal of groundwater resources to control subsidence. Therefore, risk zoning was performed using land use and the extent of operation of groundwater resources. The results showed that the north-eastern part of the aquifer has the maximum risk of subsidence. According to the obtained results from subsidence risk zoning, scenarios of reduced water withdrawal from the aquifer in its outlet were developed. The treatment strategies results showed that the maximum amount of subsidence in wet, normal and dry conditions will be 10, 14 and 18 cm, respectively. These results indicate a 14% improvement in the quantitative condition of the aquifer in wet conditions, 10% in normal conditions and 7% in dry conditions in the total aquifer of Najafabad. Improvement of conditions by simulation shows the impact of the importance of optimal utilization of groundwater resources.

scholarly journals DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Azarmi
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
World Health Organisation ◽  
Superior Performance ◽  
World Health ◽  
People Detection ◽  
Social Distancing ◽  
Online Risk ◽  
Risk Zones ◽  
Spatio Temporal

Abstract Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a generic Deep Neural Network-Based model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed model includes a YOLOv4-based framework and inverse perspective mapping for accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

scholarly journals DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020 ◽  
Author(s):  
Mahdi Rezaei ◽  
Mohsen Azarmi
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
World Health Organisation ◽  
Infection Risk ◽  
Superior Performance ◽  
World Health ◽  
Mapping Technique ◽  
People Detection ◽  
Social Distancing ◽  
Risk Zones

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a Deep Neural Network-based Model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed DNN model along with an inverse perspective mapping technique leads to a very accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document