scholarly journals Assessing Urban Greenness Fragmentation and Analysis of Its Associated Factors: A Case Study in Wuhan Metropolitan Area, China

2021 ◽  
Vol 10 (11) ◽  
pp. 760
Author(s):  
Husheng Fang ◽  
Moquan Sha ◽  
Wenjuan Lin ◽  
Dai Qiu ◽  
Zongyao Sha
Keyword(s):  
Metropolitan Area ◽  
Vegetation Cover ◽  
Associated Factors ◽  
Dominant Factor ◽  
Support Vector ◽  
Urban Vegetation ◽  
Rapid Urbanization ◽  
Wuhan Metropolitan Area ◽  
The Impact

Green vegetation plays a vital role in urban ecosystem services. Rapid urbanization often tends to induce urban vegetation cover fragmentation (UVCF) in cities and suburbs. Mapping the changes in the structure (aggregation) and abundance of urban vegetation cover helps to make improved policies for sustainable urban development. In this paper, a new distance-based landscape indicator to UVCF, Frag, was proposed first. Unlike many other landscape indicators, Frag measures UVCF by considering simultaneously both the structure and abundance of vegetation cover at local scales, and thus provides a more comprehensive perspective in understanding the spatial distribution patterns in urban greenness cover. As a case study, the urban greenness fragmentation indicated by Frag was demonstrated in Wuhan metropolitan area (WMA), China in 2015 and 2020. Support vector machine (SVM) was then designed to examine the impact on the Frag changes from the associated factors, including urbanization and terrain characteristics (elevation and slope). The Frag changes were mapped at different scales and modeled by SVM from the selected factors, which reasonably explained the Frag changes. Sensitivity analysis for the SVM model revealed that urbanization showed the most dominant factor for the Frag changes, followed by terrain elevation and slope. We conclude that Frag is an effective scale-dependent indicator to UVCF that can reflect changes in the structure and abundance of urban vegetation cover, and that modeling the impact of the associated factors on UVCF via the Frag indicator can provide essential information for urban planners.

scholarly journals Scenario Analysis on Climate Change Impacts of Urban Land Expansion under Different Urbanization Patterns: A Case Study of Wuhan Metropolitan

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Xinli Ke ◽  
Feng Wu ◽  
Caixue Ma
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Urban Expansion ◽  
Regional Climate ◽  
Urban Land ◽  
Urban Land Use ◽  
Rapid Urbanization ◽  
Urban Land Expansion ◽  
The Impact

Urban land expansion plays an important role in climate change. It is significant to select a reasonable urban expansion pattern to mitigate the impact of urban land expansion on the regional climate in the rapid urbanization process. In this paper, taking Wuhan metropolitan as the case study area, and three urbanization patterns scenarios are designed to simulate spatial patterns of urban land expansion in the future using the Partitioned and Asynchronous Cellular Automata Model. Then, simulation results of land use are adjusted and inputted into WRF (Weather Research and Forecast) model to simulate regional climate change. The results show that: (1) warming effect is strongest under centralized urbanization while it is on the opposite under decentralized scenario; (2) the warming effect is stronger and wider in centralized urbanization scenario than in decentralized urbanization scenario; (3) the impact trends of urban land use expansion on precipitation are basically the same under different scenarios; (4) and spatial distribution of rainfall was more concentrated under centralized urbanization scenario, and there is a rainfall center of wider scope, greater intensity. Accordingly, it can be concluded that decentralized urbanization is a reasonable urbanization pattern to mitigate climate change in rapid urbanization period.

scholarly journals Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Alireza Davoudi ◽  
Mohsen Ahmadi ◽  
Abbas Sharifi ◽  
Roshina Hassantabar ◽  
Narges Najafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diastolic Pressure ◽  
Systolic Pressure ◽  
Illness Severity ◽  
Support Vector ◽  
Learning Approaches ◽  
Angiotensin Converting Enzyme 2 ◽  
Decision Tree Method ◽  
The Impact

Statins can help COVID-19 patients’ treatment because of their involvement in angiotensin-converting enzyme-2. The main objective of this study is to evaluate the impact of statins on COVID-19 severity for people who have been taking statins before COVID-19 infection. The examined research patients include people that had taken three types of statins consisting of Atorvastatin, Simvastatin, and Rosuvastatin. The case study includes 561 patients admitted to the Razi Hospital in Ghaemshahr, Iran, during February and March 2020. The illness severity was encoded based on the respiratory rate, oxygen saturation, systolic pressure, and diastolic pressure in five categories: mild, medium, severe, critical, and death. Since 69.23% of participants were in mild severity condition, the results showed the positive effect of Simvastatin on COVID-19 severity for people that take Simvastatin before being infected by the COVID-19 virus. Also, systolic pressure for this case study is 137.31, which is higher than that of the total patients. Another result of this study is that Simvastatin takers have an average of 95.77 mmHg O2Sat; however, the O2Sat is 92.42, which is medium severity for evaluating the entire case study. In the rest of this paper, we used machine learning approaches to diagnose COVID-19 patients’ severity based on clinical features. Results indicated that the decision tree method could predict patients’ illness severity with 87.9% accuracy. Other methods, including the K -nearest neighbors (KNN) algorithm, support vector machine (SVM), Naïve Bayes classifier, and discriminant analysis, showed accuracy levels of 80%, 68.8%, 61.1%, and 85.1%, respectively.

scholarly journals Analysis of the Impact of Land Use on Spatiotemporal Patterns of Surface Urban Heat Island in Rapid Urbanization, a Case Study of Shanghai, China

2020 ◽  
Vol 12 (3) ◽  
pp. 1171 ◽  
Author(s):  
Hongyu Du ◽  
Fengqi Zhou ◽  
Chunlan Li ◽  
Wenbo Cai ◽  
Hong Jiang ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Heat Island ◽  
Land Surface ◽  
Agricultural Land ◽  
Heat Island ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The Impact

In the trend of global warming and urbanization, frequent extreme weather influences the life of citizens seriously. Shanghai, as a typical mega-city in China that has been successful in urbanization, suffers seriously from the urban heat island (UHI) effect. The research concentrates on the spatial and temporal pattern of surface UHI and land use. Then, the relation between them are further discussed. The results show that for the last 15 years, the UHI effect of Shanghai has been increasing continuously in both intensity and area. The UHI extends from the city center toward the suburban area. Along with the year, the ratio in area of Agricultural Land (AL), Wetland (WL), and Bare Land (BL) has decreased. On the contrary, Construction Land (CL) and Green Land (GL) have increased. The average land surface temperature (LST) rankings for each research year from high to low were all CL, BL, GL, AL, and WL. CL contributed the most to the UHI effect, while WL and GL contributed the most to mitigate the UHI. The conclusion provides practical advice aimed to mitigate the UHI effect for urban planning authorities.

scholarly journals Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing

Land ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Shanshan Hu ◽  
Yunyun Fan ◽  
Tao Zhang
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Correlation Analysis ◽  
Surface Runoff ◽  
Flood Control ◽  
Central Area ◽  
Dominant Factor ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
The Impact

The change in land use during the process of urbanization affects surface runoff and increases flood risk in big cities. This study investigated the impact of land use change on surface runoff in Beijing’s central area during the period of rapid urbanization from 1984 to 2019. Land use maps of 1984, 1999, 2009, and 2019 were generated by image classification of Landsat images. Surface runoffs were calculated with the Soil Conservation Service curve number (SCS-CN) model. Correlation analysis was used to identify the dominant factor of land use change affecting surface runoff. The result showed that the variation trend of surface runoff was consistent with the trend of impervious land in Beijing’s central area, which increased during 1984~2009 and decreased during 2009~2019. Correlation analysis showed that changes in surface runoff were most strongly correlated with changes in impervious surfaces when compared with the correlation of runoff with other types of land use. The results of this study may provide a reference for city flood control and urban planning in fast growing cities worldwide, especially in developing countries.

Integrating Employee Value Model with Churn Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 484-493
Author(s):  
Nguyen Thi Ngoc Anh ◽  
Nguyen Danh Tu ◽  
Vijender Kumar Solanki ◽  
Nguyen Linh Giang ◽  
Vu Hoai Thu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Churn Prediction ◽  
Artificial Intelligent ◽  
Weak Points ◽  
Employee Value ◽  
Value Model ◽  
The Impact

Background: In recent years, human resource management is a crucial role in every companies or organization’s operation. Loyalty employee or Churn employee influence the operation of the organization. The impact of Churn employees is difference because of their role in organization. Objective: Thus, we define two Employee Value Models (EVMs) of organizations or companies based on employee features that are popular of almost companies. Methods: Meanwhile, with the development of Artificial intelligent, machine learning is possible to give predict data-based models having high accuracy.Thus, integrating Churn prediction, EVM and machine learning such as support vector machine, logistic regression, random forest is proposed in this paper. The strong points of each model are used and weak points are reduced to help the companies or organizations avoid high value employee leaving in the future. The process of prediction integrating Churn, value of employee and machine learning are described detail in 6 steps. The pros of integrating model gives the more necessary results for company than Churn prediction model but the cons is complexity of model and algorithms and speed of computing. Results: A case study of an organization with 1470 employee positions is carried out to demonstrate the whole integrating churn predict, EVM and machine learning process. The accuracy of the integrating model is high from 82% to 85%. Moreover, the some results of Churn and value employee are analyzed. Conclusion: This paper is proposing upgrade models for predicting an employee who may leave an organization and integration of two models including employee value model and Churn prediction is feasible.

Using Machine Learning to Capture Quality Metrics from Natural Language: A Case Study of Diabetic Eye Exams

2021 ◽  
Author(s):  
Allan Fong ◽  
Nicholas Scoulios ◽  
H. Joseph Blumenthal ◽  
Ryan E. Anderson
Keyword(s):  
Logistic Regression ◽  
Natural Language ◽  
Language Processing ◽  
Synthesis Process ◽  
Support Vector ◽  
Free Text ◽  
Svm Model ◽  
Eye Exams ◽  
The Impact

Abstract Background and Objective The prevalence of value-based payment models has led to an increased use of the electronic health record to capture quality measures, necessitating additional documentation requirements for providers. Methods This case study uses text mining and natural language processing techniques to identify the timely completion of diabetic eye exams (DEEs) from 26,203 unique clinician notes for reporting as an electronic clinical quality measure (eCQM). Logistic regression and support vector machine (SVM) using unbalanced and balanced datasets, using the synthetic minority over-sampling technique (SMOTE) algorithm, were evaluated on precision, recall, sensitivity, and f1-score for classifying records positive for DEE. We then integrate a high precision DEE model to evaluate free-text clinical narratives from our clinical EHR system. Results Logistic regression and SVM models had comparable f1-score and specificity metrics with models trained and validated with no oversampling favoring precision over recall. SVM with and without oversampling resulted in the best precision, 0.96, and recall, 0.85, respectively. These two SVM models were applied to the unannotated 31,585 text segments representing 24,823 unique records and 13,714 unique patients. The number of records classified as positive for DEE using the SVM models ranged from 667 to 8,935 (2.7–36% out of 24,823, respectively). Unique patients classified as positive for DEE ranged from 3.5 to 41.8% highlighting the potential utility of these models. Discussion We believe the impact of oversampling on SVM model performance to be caused by the potential of overfitting of the SVM SMOTE model on the synthesized data and the data synthesis process. However, the specificities of SVM with and without SMOTE were comparable, suggesting both models were confident in their negative predictions. By prioritizing to implement the SVM model with higher precision over sensitivity or recall in the categorization of DEEs, we can provide a highly reliable pool of results that can be documented through automation, reducing the burden of secondary review. Although the focus of this work was on completed DEEs, this method could be applied to completing other necessary documentation by extracting information from natural language in clinician notes. Conclusion By enabling the capture of data for eCQMs from documentation generated by usual clinical practice, this work represents a case study in how such techniques can be leveraged to drive quality without increasing clinician work.

scholarly journals An Assessment of Urban Vegetation Abundance in Accra Metropolitan Area, Ghana: A Geospatial Approach

2018 ◽  
Vol 11 (1-2) ◽  
pp. 37-44 ◽  
Author(s):  
Alex Barimah Owusu
Keyword(s):  
Metropolitan Area ◽  
Vegetation Cover ◽  
Urban Areas ◽  
Rate Of Change ◽  
Urban Vegetation ◽  
City Development ◽  
Key Informants ◽  
Development Agenda ◽  
Green Vegetation ◽  
The City

Abstract The essential role played by urban vegetation in making urban areas livable is often overlooked in many developing cities. This is the case of Ghana where its capital, Accra is developing at the expense of urban vegetation. This study was conducted at the metropolitan area of Accra to estimate how the extent of vegetation cover has changed in the period of 1986-2013, using remote sensing satellite data from Landsat TM and ETM+. Furthermore, views of key informants were assessed on changes in the livability of the city of Accra which may be attributed to loss of urban green vegetation in the city. It was found that between 1986 and 2013, 42.53 km2 of vegetation was lost representing 64.6% of total vegetation in 1986. The rate of change in vegetation cover between 1986 and 1991 measured around 2.14% of the total land area annually. This however, reduced in the subsequent years measuring 0.26% between 2002 and 2008. Key informants interviewed, also believe that the loss of vegetation in the city creates livability concerns relating to ecosystem functioning, temperature rise and air quality. It is therefore recommended for urban planners and decision makers to address three critical concerns of resilience, sustainability and livability, which are the missing links in the city development agenda.

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