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Prediction of fecal coliform using logistic regression and tree-based classification models in the North Han River, South Korea

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JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 2018年第Oct.期21卷 96-108页

作者： Choi, Soo Yeon Seo, Il Won Seoul Natl Univ Dept Civil & Environm Engn 1 Gwanak Ro Seoul 08826 South Korea

In this study, data-based classification models were developed for real-time prediction of the exceedance of the safety level on fecal coliform in Daesung-ri site of North Han River. The prediction models were developed using the logistic regression model (LRM) and the tree-based models such as classification and regression model (CART), bagging model (BGM), and random forest model (RFM). For model development, rainfall, water quality, and dam discharge data from 2010 to 2015 were collected from the study site. Clustering methods were applied to reduce the sampling bias of training and test datasets and to improve the prediction accuracy. The developed four models were compared with each other in terms of prediction accuracy and applicability. The test results of developed models showed that the total correct classification rate of the four models ranged from 83.7% to 93.0%. Each classification model showed its own strengths;LRM offered flexibility by tuning cutoff values, while RFM showed the highest accuracy among the four models. The hydro-ecological process on fecal coliform could be explained by analyzing important variables in the prediction models and identifying the impacting factors through the field monitoring. The important factors both in the models and field monitoring were revealed as the rainfall-related variables, dam discharge and total phosphorus, which imply that the fecal pollution in North Han River came mainly from the rainfall events and runoff including nutrients from farmland and livestock farming in the upstream basin of Guwoon Creek and Chungpyung Dam.

关键词： Fecal coliform prediction logistic regression model Tree-based model Field monitoring North Han River

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The influence of hydrogeological and anthropogenic variables on phthalate contamination in eogenetic karst groundwater systems

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ENVIRONMENTAL POLLUTION 2018年 237卷 298-307页

作者： Torres, Norma I. Yu, Xue Padilla, Ingrid Y. Macchiavelli, Raul E. Ghasemizadeh, Reza Kaeli, David Cordero, Jose F. Meeker, John D. Alshawabkeh, Akram N. Univ Puerto Rico Dept Civil Engn & Surveying POB 9000 Mayaguez PR 00681 USA Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA Univ Puerto Rico Dept Agroenvironm Sci Mayaguez PR 00681 USA Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Univ Georgia Dept Epidemiol & Biostat Athens GA 30602 USA Univ Michigan Sch Publ Hlth Dept Environm Hlth Sci Ann Arbor MI 48109 USA

This study investigates the occurrence of six phthalates and distribution of the three most-detected phthalates in the karst region of northern Puerto Rico (KRNPR) using data from historical records and current field measurements. Statistical data analyses, including ANOVA, Chi-Square, and logistic regression models are used to examine the major factors affecting the presence and concentrations of phthalates in the KRNPR. The most detected phthalates include DEHP, DBP, and DEP. At least one phthalate specie is detected above DL in 7% of the samples and 24% of the sampling sites. Concentrations of total phthalates average 5.08 +/- 1.37 mu g L-1, and range from 0.093 to 58.4 mu g L-1. The analysis shows extensive spatial and temporal presence of phthalates resulting from dispersed phthalate sources throughout the karst aquifers. Hydrogeological factors are significantly more important in predicting the presence and concentrations of phthalates in eogenetic karst aquifers than anthropogenic factors. Among the hydrogeological factors, time of detection and hydraulic conductivities larger than 300 m d(-1) are the most influential factors. Persistent presence through time reflects continuous sources of phthalates entering the aquifers and a high capacity of the karst aquifers to store and slowly release contaminants for long periods of time. The influence of hydraulic conductivity reveals the importance of contaminant fate and transport mechanisms from contamination sources. This study improves the understanding of factors affecting the spatial variability and fate of phthalates in karst aquifers, and allows us to better predict their occurrence based on these factors. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Groundwater contamination Phthalates Karst aquifers logistic regression model

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Predicting the risk of groundwater arsenic contamination in drinking water wells

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JOURNAL OF HYDROLOGY 2018年 560卷 318-325页

作者： Cao, Hailong Xie, Xianjun Wang, Yanxin Pi, Kunfu Li, Junxia Zhan, Hongbin Liu, Peng China Univ Geosci State Key Lab Biogeol & Environm Geol Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Environm Studies Wuhan 430074 Hubei Peoples R China Texas A&M Univ Dept Geol & Geophys College Stn TX 77843 USA

Arsenic (As)-contaminated groundwater is a global concern with potential detrimental effects on the health of hundreds of millions of people worldwide. However, the extent of this problem may be more severe than anticipated, as many wells have not been tested and may contain unsafe-level of As. An optimized statistical regression model was developed to predict the probability of geogenic high As groundwater (As > 10 mu g/L) in this study. Easily obtained hydrogeochemical and geological parameters that are significantly related to As geochemical behaviors were selected as explanatory variables in the model. The results indicate that pH, Cl-, HCO3-, SO42-, and NO3- concentrations, stratigraphic information, and well depth are excellent predictors of As exposure in the Datong Basin, China. Predicted unsafe wells correspond well with the known distribution of high As groundwater in the Datong Basin. The successful application of a data set from Bangladesh also demonstrated the applicability and credibility of this proposed method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Groundwater Arsenic contamination Risk prediction logistic regression model

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model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s

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REMOTE SENSING 2019年第2期11卷 183-183页

作者： Zhao, Shangmin Zhang, Shifang Cheng, Weiming Zhou, Chenghu Taiyuan Univ Technol Coll Min Engn Dept Surveying & Mapping Taiyuan 030024 Shanxi Peoples R China Chinese Acad Sci Inst Geog & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as 'permafrost probable' (p > 0.7), 'permafrost possible' (0.7 >= p >= 0.3) and 'permafrost improbable' (p < 0.3). From the 1990s to the 2040s, the 'permafrost probable' type mainly degrades to that of 'permafrost possible', with the total area degenerating from 73.5 x 10(3) km(2) to 66.5 x 10(3) km(2). The 'permafrost possible' type mainly degrades to that of 'permafrost impossible', with a degradation area of 6.5 x 103 km(2), which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions.

关键词： mountain permafrost logistic regression model model simulation and prediction mean decadal air temperature data Qilian Mountains

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Influence of polymorphisms in toll-like receptors (TLRs) on malaria susceptibility in low-endemic area of the Atlantic Forest, Sao Paulo, Brazil

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ACTA TROPICA 2018年 182卷 309-316页

作者： Guimaraes, Lilian O. Fernandes, Francisco Monteiro, Eliana F. Curado, Izilda Holcman, Marcia M. Wunderlich, Gerhard Santos, Sidney E. Soler, Julia M. Kirchgatter, Karin Univ Sao Paulo Inst Trop Med Superintendence Endem Dis Control Malaria Res Ctr BR-05403000 Sao Paulo Brazil Univ Sao Paulo Inst Math & Stat Dept Stat BR-05508090 Sao Paulo Brazil Superintendence Endem Dis Control Lab Immunoepidemiol BR-01027000 Sao Paulo Brazil Superintendence Endem Dis Control Div Vector Control BR-01027000 Sao Paulo Brazil Univ Sao Paulo Inst Biomed Sci Dept Parasitol BR-05508000 Sao Paulo Brazil Fed Univ Para Inst Biol Sci Lab Human & Med Genet BR-66075110 Belem Para Brazil

In low-endemic areas for malaria transmission, asymptomatic individuals play an important role as reservoirs for malarial infection. Understanding the dynamics of asymptomatic malaria is crucial for its efficient control in these regions. Genetic host factors such as Toll-like receptor CUR) polymorphisms may play a role in the maintenance or elimination of infection. In this study, the effect of TLR polymorphisms on the susceptibility to malaria was investigated among individuals living in the Atlantic Forest of Sao Paulo, Southern Brazil. A hundred and ninety-five Brazilian individuals were enrolled and actively followed up for malaria for three years. Twenty-four polymorphisms in five toll-like receptor (TLR) genes were genotyped by RFLP, direct sequencing or fragment analysis. The genotypes were analyzed for the risk of malaria. Ongoing Plasmodium vivax or P. malaria infection, was identified by the positive results in PCR tests and previous P. vtvax malaria, was assumed when antiplasmodial antibodies against PvMSP1(19) were detected by ELISA. An evaluation of genomic ancestry was conducted using biallelic ancestry informative markers and the results were used as correction in the statistical analysis. Nine SNPs and one microsatellite were found polymorphic and three variant alleles in TLR genes were associated to malaria susceptibility. The regression coefficient estimated for SNP TLR9.-1237.T/C indicated that the presence of at least one allele C increased, on average, 2.3 times the malaria odds, compared to individuals with no allele C in this SNP. However, for individuals with the same sex, age and household, the presence of at least one allele C in SNP TLR9.-1486.T/C reduced, on average, 1.9 times the malaria odds, compared to individuals with no allele C. Moreover, this allele C plus an S allele in TLR6.P249S in individuals with same sex, age and ancestry, reduced, on average, 4.4 times the malaria odds. Our findings indicate a significant association of TL

关键词： Malaria Toll-like receptors Genetic polymorphism Association study logistic regression model Principal components Population structure correction

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Data mining for modeling students' performance: A tutoring action plan to prevent academic dropout

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 66卷 541-556页

作者： Burgos, Concepcion Campanario, Maria L. de la Pena, David Lara, Juan A. Lizcano, David Martinez, Maria A. MOU Fac Ciencias Econ & Empresariales Ctra CorunaKm 38-500Via Serv 15 Madrid 28400 Spain MOU Escuela Ciencias Tecn & Ingn Ctra CorunaKm 38-500Via Serv 15 Madrid 28400 Spain

E-learning systems generate huge amounts of data, whose analysis may become a daunting task which makes it necessary to use computational analytical techniques and tools. We propose the use of knowledge discovery techniques to analyse historical student course grade data in order to predict whether or not a student will drop out of a course. logistic regression models are used for the purpose of classification. Experiments conducted with data on over 100 students for several distance learning courses confirm the predictive power of our proposal. Using the resulting predictive models we have designed a tutoring action plan. Applying this plan, we managed to reduce the dropout rate by 14% with respect to previous academic years in which no dropout prevention mechanism was applied. Our main contribution is a tool and a tutoring plan that can be used by our educational institution (and others) to reduce dropout rate in e-learning courses. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： E-learning Student dropout prediction Educational data mining logistic regression model Temporal data Student dropout prevention Tutoring action plan

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A Data Driven Cycle Time Prediction With Feature Selection in a Semiconductor Wafer Fabrication System

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2018年第1期31卷 173-182页

作者： Wang, Junliang Zhang, Jie Wang, Xiaoxi Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China

Accurate prediction of cycle time (CT) plays an important role in the promises of a good delivery-time for semiconductor manufacturers. However, the CTs of wafer lots are difficult to forecast since they are influent by a lot factors (e.g., workload for every machine). The identification of key factors (called CT-related) can not only improve the prediction performance but also facilitate the control of CT, which is of particular significance. This paper focuses on gathering subtle variables into the candidate variables set along with the further analysis which is required in the correlation analysis between various candidate factors and CTs of wafer lots. The candidates set is first constructed to collect all factors that may affect wafer lots' CT. Then, a regression-based model is proposed to select CT-related variables, which consists of three parts: 1) discretization;2) adaptive logistic regression-based correlation analysis;and 3) CT-related factor selection. Subsequently, a parallel computation model is implemented to forecast the CTs of wafer lots. In the numerical experiments, 108 CT-related factors stood out from 774 candidates and replaced six global factors (used as reference) to predict CTs of wafer lots. The results indicated that the proposed approach had higher accuracy than linear regression and back propagation network in CT forecasting in large scale data analysis.

关键词： Cycle time forecasting data driven semiconductor manufacturing logistic regression model

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modeling clustered long-term survivors using marginal mixture cure model

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BIOMETRICAL JOURNAL 2018年第4期60卷 780-796页

作者： Niu, Yi Song, Lixin Liu, Yufeng Peng, Yingwei Dalian Univ Technol Sch Math Sci Dalian Liaoning Peoples R China Queens Univ Dept Publ Hlth Sci Kingston ON K7L 3N6 Canada Queens Univ Dept Math & Stat Kingston ON Canada Queens Univ Canc Care & Epidemiol Canc Res Inst Kingston ON Canada

There is a great deal of recent interests in modeling right-censored clustered survival time data with a possible fraction of cured subjects who are nonsusceptible to the event of interest using marginal mixture cure models. In this paper, we consider a semiparametric marginal mixture cure model for such data and propose to extend an existing generalized estimating equation approach by a new unbiased estimating equation for the regression parameters in the latency part of the model. The large sample properties of the regression effect estimators in both incidence and the latency parts are established. The finite sample properties of the estimators are studied in simulation studies. The proposed method is illustrated with a bone marrow transplantation data and a tonsil cancer data.

关键词： ES algorithm generalized estimating equations logistic regression model proportional hazards model sandwich variance estimation

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Habitat selection and prediction of the spatial distribution of the Chinese horseshoe bat (R-sinicus) in the Wuling Mountains

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2019年第1期191卷 4-4页

作者： Liang, Liang Luo, Xiang Liu, Zhixiao Wang, Jiahui Huang, Ting Li, Erzhu Jiangsu Normal Univ Sch Geog Geomat & Planning Xuzhou 221116 Jiangsu Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing 210008 Jiangsu Peoples R China Jishou Univ Coll Biol & Environm Sci Jishou 416000 Hunan Peoples R China

Habitat selection by the Chinese horseshoe bat (Rhinolophus sinicus) in the Wuling Mountains was studied in this paper. Global positioning system (GPS), remote sensing (RS) and geographic information system (GIS) technologies were used to obtain ground survey data and analyse the habitat factors driving the distribution of R. sinicus. Based on these basic data, a binary logistic regression method was used to establish habitat selection models of R. sinicus. Then, the corrected Akaike information criterion (AIC(C)) was used to screen an optimal model, and the Hosmer-Lemeshow test indicated that the optimal model has suitable goodness of fit. Finally, the optimal model was used to predict the spatial distribution of R. sinicus in the Wuling Mountains. Verification analysis showed that the overall accuracy of the model was 72.7% and that the area under the curve (AUC) value was 0.947, which indicated that the model was effective for predicting suitable habitat for R. sinicus. The model results also showed that the main factors that influenced habitat selection were slope, annual mean temperature and distances from roads, rivers and residential land. R. sinicus preferred areas far from roads and residential land and areas near rivers. Generally, higher values of slope and annual mean temperature were associated with a greater likelihood of R. sinicus presence. Therefore, the protection of the water bodies surrounding R. sinicus habitats and fully addressing the impacts of human activities on R. sinicus habitats are recommended to protect the survival and reproduction of the population.

关键词： Habitat selection Spatial distribution prediction Rhinolophus sinicus logistic regression model

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Fixed-width confidence interval for covariate-adjusted response-adaptive designs

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2018年第2期70卷 353-371页

作者： Bandyopadhyay, Uttam Biswas, Atanu Univ Calcutta Dept Stat 35 Ballygunge Circular Rd Kolkata 700019 India Indian Stat Inst Appl Stat Unit 203 BT Rd Kolkata 700108 India

In this paper, we obtain fixed-width confidence interval for covariate-adjusted response-adaptive designs. Specifically, we consider logistic regression model and the normal regression model for binary and continuous responses, respectively, both in the situations for presence and absence of treatment-covariate interactions. Simulation study and real-data analysis are carried out.

关键词： Coverage probability Fixed-width confidence interval logistic regression model Normal regression model Stopping time Treatment-covariate interaction

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