scholarly journals Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1299
Author(s):  
Xuhong Yang ◽  
Xiaobin Jin ◽  
Yinkang Zhou
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Environmental Variables ◽  
Climatic Conditions ◽  
Assessment Model ◽  
Hunan Province ◽  
Practical Significance ◽  
Wildfire Risk ◽  
Forest Density ◽  
Wildfire Occurrence

Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as the case area, this study used remotely sensed high-temperature fire data as the wildfire sample. Twelve factors related to topography, climatic conditions, vegetation attributes, and human activities were used as environmental variables affecting wildfire occurrence. Then, a Maxent wildfire risk assessment model was constructed with GIS, which analyzed the contribution, importance, and response of environmental variables to wildfire in Hunan Province. The results show that (1) the Maxent model has high applicability and feasibility when applied to wildfire risk assessment after a test of wildfire sample sites; (2) the importance of meteorological conditions and vegetation status variables to wildfire are 54.64% and 25.40%, respectively, and their contribution to wildfire are 43.03% and 34.69%, respectively. The interaction between factors can enhance or weaken the contribution of factors on wildfire. (3) The mechanism for the effects of environmental variables on wildfire is not linear as generally believed; temperature, aridity, land use type, GDP, distance from the road, and population density have a nonlinear positive correlation with the probability of wildfire occurrence. Elevation, slope, precipitation, wind speed, and vegetation cover within the suitable interval positively contribute to the probability of wildfire, while the environmental conditions outside the suitable interval curb the occurrence of wildfire. The response of wildfire probability to forest density is U-shaped, which means either too high or too low will promote the occurrence of wildfire. (4) There is geographical variation of wildfire risk in Hunan Province. The areas at high risk and below account for 74.48% of the total area, while the areas at significantly high risk and above account for a relatively low proportion, 25.52%.

scholarly journals Wildfire Risk Assessment of Transmission-Line Corridors Based on Naïve Bayes Network and Remote Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 634
Author(s):  
Weijie Chen ◽  
You Zhou ◽  
Enze Zhou ◽  
Zhun Xiang ◽  
Wentao Zhou ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Transmission Line ◽  
Naive Bayes ◽  
Southern China ◽  
Naïve Bayes ◽  
Assessment Model ◽  
Related Factors ◽  
Wildfire Risk ◽  
Wildfire Occurrence ◽  
Bayes Network

Considering the complexity of the physical model of wildfire occurrence, this paper develops a method to evaluate the wildfire risk of transmission-line corridors based on Naïve Bayes Network (NBN). First, the data of 14 wildfire-related factors including anthropogenic, physiographic, and meteorologic factors, were collected and analyzed. Then, the relief algorithm is used to rank the importance of factors according to their impacts on wildfire occurrence. After eliminating the least important factors in turn, an optimal wildfire risk assessment model for transmission-line corridors was constructed based on the NBN. Finally, this model was carried out and visualized in Guangxi province in southern China. Then a cost function was proposed to further verify the applicability of the wildfire risk distribution map. The fire events monitored by satellites during the first season in 2020 shows that 81.8% of fires fall in high- and very-high-risk regions.

scholarly journals An Alternative Risk Assessment Model of Urban Waterlogging: A Case Study of Ningbo City

2021 ◽  
Vol 13 (2) ◽  
pp. 826
Author(s):  
Meiling Zhou ◽  
Xiuli Feng ◽  
Kaikai Liu ◽  
Chi Zhang ◽  
Lijian Xie ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Assessment Model ◽  
Risk Assessments ◽  
Risk Assessment Model ◽  
Risk Area ◽  
The North ◽  
Coastal Cities ◽  
Risk Areas ◽  
The City

Influenced by climate change, extreme weather events occur frequently, and bring huge impacts to urban areas, including urban waterlogging. Conducting risk assessments of urban waterlogging is a critical step to diagnose problems, improve infrastructure and achieve sustainable development facing extreme weathers. This study takes Ningbo, a typical coastal city in the Yangtze River Delta, as an example to conduct a risk assessment of urban waterlogging with high-resolution remote sensing images and high-precision digital elevation models to further analyze the spatial distribution characteristics of waterlogging risk. Results indicate that waterlogging risk in the city proper of Ningbo is mainly low risk, accounting for 36.9%. The higher-risk and medium-risk areas have the same proportions, accounting for 18.7%. They are followed by the lower-risk and high-risk areas, accounting for 15.5% and 9.6%, respectively. In terms of space, waterlogging risk in the city proper of Ningbo is high in the south and low in the north. The high-risk area is mainly located to the west of Jiangdong district and the middle of Haishu district. The low-risk area is mainly distributed in the north of Jiangbei district. These results are consistent with the historical situation of waterlogging in Ningbo, which prove the effectiveness of the risk assessment model and provide an important reference for the government to prevent and mitigate waterlogging. The optimized risk assessment model is also of importance for waterlogging risk assessments in coastal cities. Based on this model, the waterlogging risk of coastal cities can be quickly assessed, combining with local characteristics, which will help improve the city’s capability of responding to waterlogging disasters and reduce socio-economic loss.

scholarly journals On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors

2018 ◽  
Vol 7 (9) ◽  
pp. 354 ◽  
Author(s):  
Xun Zhang ◽  
Min Jin ◽  
Jingying Fu ◽  
Mengmeng Hao ◽  
Chongchong Yu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Southeast Asia ◽  
The Philippines ◽  
Assessment Model ◽  
Terrorist Attacks ◽  
Recommendation Algorithm ◽  
Spatial Risk ◽  
Risk Areas ◽  
F Measure

Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas.

A Validated Risk Model for Prediction of Early Readmission in Patients with Hepatic Encephalopathy

2018 ◽  
Vol 17 (5) ◽  
pp. 0-10
Author(s):  
Andrew J. Kruger ◽  
Fasika Aberra ◽  
Sylvester M. Black ◽  
Alice Hinton ◽  
James Hanje ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hepatic Encephalopathy ◽  
High Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Low Risk ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Early Readmission ◽  
The Impact

Introduction and aim. Hepatic encephalopathy (HE) is a common complication in cirrhotics and is associated with an increased healthcare burden. Our aim was to study independent predictors of 30-day readmission and develop a readmission risk model in patients with HE. Secondary aims included studying readmission rates, cost, and the impact of readmission on mortality. Material and methods. We utilized the 2013 Nationwide Readmission Database (NRD) for hospitalized patients with HE. A risk assessment model based on index hospitalization variables for predicting 30-day readmission was developed using multivariate logistic regression and validated with the 2014 NRD. Patients were stratified into Low Risk and High Risk groups. Cox regression models were fit to identify predictors of calendar-year mortality. Results. Of 24,473 cirrhosis patients hospitalized with HE, 32.4% were readmitted within 30-days. Predictors of readmission included presence of ascites (OR: 1.19; 95% CI: 1.06-1.33), receiving paracentesis (OR: 1.43; 95% CI: 1.26-1.62) and acute kidney injury (OR: 1.11; 95% CI: 1.00-1.22). Our validated model stratified patients into Low Risk and High Risk of 30-day readmissions (29% and 40%, respectively). The cost of the first readmission was higher than index admission in the 30-day readmission cohort ($14,198 vs. $10,386; p-value < 0.001). Thirty-day readmission was the strongest predictor of calendar-year mortality (HR: 4.03; 95% CI: 3.49-4.65). Conclusions. Nearly one-third of patients with HE were readmitted within 30-days, and early readmission adversely impacted healthcare utilization and calendar-year mortality. With our proposed simple risk assessment model, patients at high risk for early readmissions can be identified to potentially avert poor outcomes.

External validation of the IMPROVE Bleeding Risk Assessment Model in medical patients

2016 ◽  
Vol 116 (09) ◽  
pp. 530-536 ◽  
Author(s):  
David J. Rosenberg ◽  
Anne Press ◽  
Joanna Fishbein ◽  
Martin Lesser ◽  
Lauren McCullagh ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
High Risk Patient ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Validation Sample ◽  
Medical Patients ◽  
Medically Ill ◽  
Major Bleed ◽  
Hospitalised Patients

SummaryThe IMPROVE Bleed Risk Assessment Model (RAM) remains the only bleed RAM in hospitalised medical patients using 11 clinical and laboratory factors. The aim of our study was to externally validate the IMPROVE Bleed RAM. A retrospective chart review was conducted between October 1, 2012 and July 31, 2014. We applied the point scoring system to compute risk scores for each patient in the validation sample. We then dichotomised the patients into those with a score <7 (low risk) vs ≥ 7 (high risk), as outlined in the original study, and compared the rates of any bleed, non-major bleed, and major bleed. Among the 12,082 subjects, there was an overall 2.6 % rate of any bleed within 14 days of admission. There was a 2.12 % rate of any bleed in those patients with a score of < 7 and a 4.68 % rate in those with a score ≥ 7 [Odds Ratio (OR) 2.3 (95 % CI=1.8–2.9), p<0.0001]. MB rates were 1.5 % in the patients with a score of < 7 and 3.2 % in the patients with a score of ≥ 7, [OR 2.2 (95 % CI=1.6–2.9), p<0.0001]. The ROC curve was 0.63 for the validation sample. This study represents the largest externally validated Bleed RAM in a hospitalised medically ill patient population. A cut-off point score of 7 or above was able to identify a high-risk patient group for MB and any bleed. The IMPROVE Bleed RAM has the potential to allow for more tailored approaches to thromboprophylaxis in medically ill hospitalised patients.Supplementary Material to this article is available online at www.thrombosis-online.com.

Research into behavioral traits of the susceptibility to the risk of significant financial misstatements

2020 ◽  
Vol 23 (3) ◽  
pp. 262-275
Author(s):  
S.V. Arzhenovskii ◽  
T.G. Sinyavskaya ◽  
A.V. Bakhteev
Keyword(s):  
Risk Assessment ◽  
Statistical Analysis ◽  
High Risk ◽  
Financial Reporting ◽  
Influential Factors ◽  
Assessment Model ◽  
Expert Survey ◽  
Personal Traits ◽  
Behavioral Traits ◽  
The Impact

Subject. The article identifies behavioral signs of the susceptibility to the risk of material misstatements through the expert survey of professional auditors. Objectives. We do empirical research into the impact five behavioral traits have, which we discovered through the two parameter risk assessment model, i.e. tolerance to violation of laws, money pathology, susceptibility to high risk, aspiration of impunity and legislative illiteracy in finance. Method.s We performed the expert survey of professional auditors to discover what determines the susceptibility to fraud among those charged with financial reporting. The expert group was made on the basis of an unbiased approach and documentation. We applied the Rasch model to rank personal traits. The collected data were processed with methods of descriptive statistics and multivariate statistical analysis. Results. Carrying out the statistical analysis of experts’ opinions, we found that their significantly correlated. Personal traits were sorted by their impact on risk assessment. Money pathology, susceptibility to high risk, aspiration of impunity and legislative illiteracy in finance were acknowledged as the most influential factors in terms of the susceptibility to misstatements of financial reporting. Conclusions and Relevance. We empirically proved the importance of factors influencing the propensity to risk of misstating financial reports. We used our own theoretical concept. The findings can be useful to auditing forms to detect the customers’ propensity to the risk of manipulating financial reporting.

Human resources allocation in oncologic day hospital: Validation of the Kohen-Gil Deza scale (KGD) for risk assesment.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 6596-6596
Author(s):  
Nelson Kohen ◽  
Ernesto Gil Deza ◽  
Natasha Gercovich ◽  
Eduardo L. Morgenfeld ◽  
Carlos Fernando Garcia Gerardi ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Human Resources ◽  
Symptom Control ◽  
Low Risk ◽  
Assessment Model ◽  
Resources Allocation ◽  
Intermediate Risk ◽  
Day Hospital ◽  
Academic Qualification

6596 Background: The oncological day hospital (ODH) at IOHM carries out 80 chemotherapies per day with 6 certified oncological nurses as staff. Human resources allocation in oncology has not been formally studied in relation to treatment risks. The objective of this paper is to present a risk assessment model for the rational allocation for human resources in the ODH using the KGD scale. Methods: The KGD scale was designed through a retrospective evaluation of more than 15,000 treatments (Tx). Between November 1st and December 1st, 2012, this instrument was validated with all new patients (Pt) beginning Tx at IOHM. The KGD scale evaluates risk according to: Five Pt characteristics (Elderly, Polymedicated, Without symptom control, Neuropsychiatric problems, Presence or absence of family members); Four Tx characteristics (New drugs, Complex protocol, High risk of acute toxicity, Infrequently used) and workplace context(New personnel, Holiday absences, With or without close medical support). The KGD scale was determined for each Tx and applied as follows: Low Risk (0-3 points): two nurses in the ODH, supervision is at the patient’s request and the chemotherapy can be administered at the beginning or end of the workday; Intermediate Risk (4-5 points): three nurses in the ODH, supervision is mandatory and the treatment can take place at any time in the workday; High Risk(6 or more points): four nurses in the ODH, supervision must be constant and the Tx must take place in the middle of the workday. The chemotherapy outcome was observed. Results: One hundred and thirty patients were admitted. Sex fem 74 (59%), male 56 (41%): age: 49y (range 22-87). Diagnosis: breast 40, colon: 21, lung: 16, ovaries:11, lymphoma: 11, testis:7, sarcoma: 5 ; others: 19 KGD risk assessment: Low Risk 25 pts (19 %); Intermediate Risk 77 pts (59%); High Risk 28 pts (21%). There were no complications in any of the 312 chemotherapy treatments administered to this cohort. Conclusions: 1) The KGD scale has shown to be a useful aid in the treatment risk assessment. 2) Use of the KGD scale allows for an efficient personnel allocation at the ODH according the Tx risk 3) The academic qualification of the nurses staff are mandatory to control the risk.

scholarly journals The Risk Characteristics of Heavy Metals in Urban Soil

2021 ◽  
Author(s):  
Yan Li ◽  
Dike Feng ◽  
Meiying Ji ◽  
Zhanpeng Li ◽  
Ruocheng Zhang ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Heavy Metal ◽  
High Risk ◽  
Human Health ◽  
Rapid Development ◽  
Risk Index ◽  
Principal Component ◽  
Research Area ◽  
Assessment Model

Abstract With the rapid development of China's industrial economy, heavy metals and other pollutants continue to accumulate in the environment, which has created serious threats for the ecological environment and human health. To comprehensively evaluate the ecological risks from heavy metals in the soil in Nanjing, China, as well as the status of the risks to human health, this study randomly collected 50 surface soil samples, and the contents of Al, Ca, Fe, Mg, Mn, Ni, Ti, Cd, Cr, Cu, Pb and Zn in the samples were determined, combined with the ecological risk index and the USEPA health risk assessment model for a comprehensive risk assessment of soil heavy metals in Nanjing. The results show that there has been heavy metal enrichment of Mn, Pb, Zn and other heavy metals in the research area in Nanjing city, and the variation coefficients of Pb and Cu are distinctly large; that is, the distribution of Pb and Cu in the research area shows a great fluctuation. These elements are all slightly polluting, among which the Cu heavy metal pollution degree is different, and Pb element pollution is the most serious. Children are at a high risk of exposure in various ways, among which Pb and Cu elements have a high risk of causing non-carcinogenic issues. Overall, Pb and Cu in Nanjing are important risk elements that should be monitored and controlled. The results of the correlation analysis showed that the content changes of Pb, Zn and Cu; Ni, Ti and Fe; and Zn and Pb had extremely significant correlations, indicating that they may have the same source; while Ti and Ca, Ti and Cu, and Pb and Zn showed opposite changes, indicating that their concentrations were inversely related. The results of the principal component analysis showed that industrial sources in Nanjing contributed the most heavy metals, reaching 34.4%. The second largest source was from parent material and fertilizer, which contributed 32.3% and 19.6%, respectively. The sources with the lowest contributions were from weathering and deposition, which reached 13.7%.

Validation of risk assessment models predicting venous thromboembolism in inpatients with AECOPD: a multicenter cohort study

2021 ◽  
Author(s):  
Chen Zhou ◽  
Qun Yi ◽  
Huiqing Ge ◽  
Hailong Wei ◽  
Huiguo Liu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Assessment Model ◽  
Risk Level ◽  
Chronic Obstructive ◽  
Multicenter Cohort Study ◽  
Mortality And Morbidity ◽  
Increased Risk ◽  
Study Population

Background: As inpatients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are at increased risk for venous thromboembolism (VTE), identifying high-risk patients requiring thromboprophylaxis is critical to reduce the mortality and morbidity associated with VTE. This study aimed to evaluate and compare the validities of the Padua Prediction Score and Caprini risk assessment model (RAM) in predicting the risk of VTE in inpatients with AECOPD. Methods: The inpatients with AECOPD were prospectively enrolled from seven medical centers of China between September 2017 and January 2020. Caprini and Padua scores were calculated on admission, and the incidence of 3-month VTE was investigated. Results: Among the 3277 eligible patients with AECOPD, 128 patients (3.9%) developed VTE within 3 months after admission. The distribution of the study population by the Caprini risk level was as follows: high, 53.6%; moderate, 43.0%; and low, 3.5%. The incidence of VTE increased by risk level as high, 6.1%; moderate, 1.5%; and low, 0%. According to the Padua RAM, only 10.9% of the study population was classified as high risk and 89.1% as low risk, with the corresponding incidence of VTE 7.9% and 3.4%, respectively. The Caprini RAM had higher area under curve (AUC) compared with the Padua RAM (0.713  0.021 vs 0.644 ± 0.023, P = 0.029). Conclusion: The Caprini RAM was superior to the Padua RAM in predicting the risk of VTE in inpatients with AECOPD and might better guide thromboprophylaxis in these patients.

scholarly journals The Application of Gail Model to Predict the Risk of Developing Breast Cancer among Jordanian Women

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Hikmat Abdel-Razeq ◽  
Luna Zaru ◽  
Ahmed Badheeb ◽  
Shadi Hijjawi
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
High Risk ◽  
Risk Score ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Breast Cancer Patients ◽  
Gail Model ◽  
History Of ◽  
The Mean

Background and Objectives. Breast cancer has been the most common cancer affecting women in Jordan. In the process of implementing breast cancer prevention and early detection programs, individualized risk assessment can add to the cost-effectiveness of such interventions. Gail model is a widely used tool to stratify patients into different risk categories. However, concerns about its applicability across different ethnic groups do exist. In this study, we report our experience with the application of a modified version of this model among Jordanian women. Methods. The Gail risk assessment model (RAM) was modified and used to calculate the 5-year and lifetime risk for breast cancer. Patients with known breast cancer were used to test this model. Medical records and hospital database were utilized to collect information on known risk factors. The mean calculated risk score for women tested was 0.65. This number, which corresponds to the Gail original score of 1.66, was used as a cutoff point to categorize patients as high risk. Results. A total of 1786 breast cancer patients with a mean age of 50 (range: 19–93) years were included. The modified version of the Gail RAM was applied on 1213 patients aged 35–59.9 years. The mean estimated risk for developing invasive breast cancer over the following five years was 0.54 (95% CI: 0.52, 0.56), and the lifetime risk was 3.42 (95% CI: 3.30, 3.53). Only 210 (17.3%) women had a risk score >0.65 and thus categorized as high risk. First-degree family history of breast cancer was identified among 120 (57.1%) patients in this high-risk group. Conclusions. Among a group of patients with an established diagnosis of breast cancer, a modified Gail risk assessment model would have been able to stratify only 17% into the high-risk category. The family history of breast cancer contributed the most to the risk score.

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