Jackknife variance of the partial area under the empirical receiver operating characteristic curve

Bandos, Andriy I.; Guo, Ben; Gur, David

doi:10.1177/0962280214551190

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…In general, the jackknife test was used to better understand the pattern of gully erosion. In particular, the AUC-based statistical coefficient is reliable on the jackknife test, which accepts practical problems in a broader sense [109]. This test identified the most important conditioning factors in a particular model and calibrated all parameters [110].…”

Section: Measuring the Importance Of Gecfs By The Jackknife Testmentioning

confidence: 99%

Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

et al. 2020

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The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.

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Section: Measuring the Importance Of Gecfs By The Jackknife Testmentioning

confidence: 99%

Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

et al. 2020

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“…where v is the variance estimator, G i is the Gini coefficient for the i th observation of the sample, and N is the sample size ( 18 , 23 , 24 ). The t-test has been used to verify the existence of inequality in the distribution of resources, which is the null hypothesis of this test.…”

Section: Methodsmentioning

confidence: 99%

Trends of geographic inequalities in the distribution of human resources in healthcare system: the case of Iran

Sefiddashti¹,

Arab²,

Ghazanfari³

et al. 2016

Electron physician

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IntroductionConsidering the scarcity of skilled workers in the health sector, the appropriate distribution of human resources in this sector is very important for improving people’s health. Having information about the degree of equality in the distribution of health human resources and their time trends is necessary for better planning and efficient use of these resources. The aim of this study was to determine the trend of inequality in the allocation of human resources in the health sector in Tehran between 2007 and 2013.MethodsThis cross-sectional study was conducted in Tehran Province in Iran. The inequality in the distribution of human resources (specialists, general practitioners, pharmacists, paramedics, dentists, nurses and community health workers (Behvarz)) in 10 cities in Tehran Province was investigated using the Gini coefficient and the dissimilarity index. The time trend of inequality was examined by regression analysis. The required data were collected from the statistical yearbook of the Iran Statistics Center (ISC).ResultsThe highest value of the Gini coefficient (GC) was related to nurses (GC = 0.291) in 2007. The highest value of the Gini coefficient was related to nurses and Behvarzs in 2008 and 2009, respectively. The distribution of specialists had the highest inequality in 2010 (GC = 0.298), 2011 (GC = 0.300) and 2013 (GC = 0.316). General practitioners had the lowest Gini coefficient for 2007, 2008 and 2012. Nurses for 2009 and Behvarzs for 2010, 2011 and 2013 had the lowest value of Gini coefficient. The dissimilarity indexes for specialists and general practitioners were 26.64 and 8.72 in 2013, respectively. The means of this index for included resources were 31.35, 18.27, 16.91, 22.32, 15.82, 26.74, and 24.33, respectively. The time trend analysis showed that the coefficient of time was positive for all of the human resources, except Behvarzes, and only the coefficient of general practitioners was statistically significant ( p<0.01).ConclusionOver time, inequalities in the distribution of resources in the health sector have been increasing. By developing the private sector and considering the trend of this sector to operate in the more developed regions, health policy makers should continually evaluate the distribution of human resources, and they should arrange a specific plan for the allocation of human resources in the health sector.

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“…In this study, the sensitivity of flood conditioning factors was analysed by the jackknife test Park No-Wook (2015). It is believed to have a high capability to deal with a wide range of practical problems (Bandos et al 2017). This test is done using the percentage of relative decrease of the…”

Section: Sensitivity Analysismentioning

confidence: 99%

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

Yaseen

Chen

2022

Stoch Environ Res Risk Assess

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Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flooding are different for every region as they depend upon the characteristics of each region. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of the ensemble model showed 99% accuracy in training and 98% accuracy in validation datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.

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Jackknife variance of the partial area under the empirical receiver operating characteristic curve

Cited by 12 publications

References 17 publications

Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

Trends of geographic inequalities in the distribution of human resources in healthcare system: the case of Iran

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

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