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Desertification status mapping in MuttumaWatershed by using random forest model

Research in Cold and Arid Regions 2022年第1期14卷 32-42页

作者： S.Dharumarajan Thomas F.A.Bishop ICAR-National Bureau of Soil Survey and Land Use Planning Regional CentreHebbalBangalor 560024India Faculty of Agriculture and Environment The University of SydneySydneyNSW2006Australia

Potential of the random forest model on mapping of different desertification processes was studied in Muttuma watershed of mid-Murrumbidgee river region of New South Wales,Australia.Desertification vulnerability index was developed using climate,terrain,vegetation,soil and land quality indices to identify environmentally sensitive areas for desertification.random forest model(RFM)was used to predict the different desertification processes such as soil erosion,salinization and waterlogging in the watershed and the information needed to train classification algorithms was obtained from satellite imagery interpretation and ground truth data.Climatic factors(evaporation,rainfall,temperature),terrain factors(aspect,slope,slope length,steepness,and wetness index),soil properties(pH,organic carbon,clay and sand content)and vulnerability indices were used as an explanatory variable.Classification accuracy and kappa index were calculated for training and testing datasets.We recorded an overall accuracy rate of 87.7%and 72.1%for training and testing sites,respectively.We found larger discrepancies between overall accuracy rate and kappa index for testing datasets(72.2%and 27.5%,respectively)suggesting that all the classes are not predicted well.The prediction of soil erosion and no desertification process was good and poor for salinization and water-logging process.Overall,the results observed give a new idea of using the knowledge of desertification process in training areas that can be used to predict the desertification processes at unvisited areas.

关键词： desertification processes vulnerability indices random forest model extrapolation

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Quantification of Central and Eastern China's atmospheric CH_(4) enhancement changes and its contributions based on machine learning approach

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Journal of Environmental Sciences 2024年第4期138卷 236-248页

作者： Xinyue Ai Cheng Hu Yanrong Yang Leying Zhang Huili Liu Junqing Zhang Xin Chen Guoqiang Bai Wei Xiao College of Biology and the Environment Joint Center for sustainable Forestry in Southern ChinaNanjing Forestry UniversityNanjing 210037China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China Guang’an Vocational&Technical College Guangan 638550China HuaNan Meteorological Administration Huanan 154400China

Methane is the second largest anthropogenic greenhouse gas,and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks.Therefore,the monitoring of CH_(4) concentration changes and the assessment of underlying driving factors can provide scientific basis for the government’s policy making and evaluation.China is the world’s largest emitter of anthropogenic methane.However,due to the lack of ground-based observation sites,little work has been done on the spatial-temporal variations for the past decades and influencing factors in China,especially for areas with high anthropogenic emissions as Central and Eastern China.Here to quantify atmospheric CH_(4) enhancements trends and its driving factors in Central and Eastern China,we combined the most up-to-date TROPOMI satellite-based column CH_(4)(xCH_(4))concentration from 2018 to 2022,anthropogenic and natural emissions,and a random forest-based machine learning approach,to simulate atmospheric xCH_(4) enhancements from 2001 to 2018.The results showed that(1)the random forest model was able to accurately establish the relationship between emission sources and xCH_(4) enhancement with a correlation coefficient(R^(2))of 0.89 and a root mean-square error(RMSE)of 11.98 ppb;(2)The xCH_(4) enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018,with a relative change of 3.27%±0.13%;(3)The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH_(4) enhancement,contributing 68.00% and 31.21%,respectively,and the decrease of animal ruminants contributed-6.70% of its enhancement trend.

关键词： TROPOMI Methane column concentrations Anthropogenic sources random forest model

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Study of ground ozone and precursors along with particulate matter at residential sites in the vicinity of power plant

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Waste Disposal and Sustainable Energy 2023年第4期5卷 535-549页

作者： Asha B.Chelani Rahul Vyawahare Sneha Gautam Air Pollution Control Division CSIR-National Environmental Engineering Research Institute(CSIR-NEERI)NagpurIndia Department of Civil Engineering Karunya Institute of Technology and SciencesCoimbatoreTamil Nadu641114India

Emission source characterization and meteorological influence are the key aspects to gain insight into the ground ozone governing mechanisms.Receptor-based data analysis techniques help in comprehending local ozone fluctuations in the lack of accurate information on the emission characteristics.Through sophisticated data analysis,the current study offers insight into the key factors influencing the ozone changes in the vicinity of power plants.Ground ozone(O_(3))and its precursor variables carbon monoxide(CO),nitric oxide(NO),nitrogen dioxide(NO_(2)),Sulphur dioxide(SO_(2)),benzene,toluene,ethyl-benzene and xylene(BTEX)along with the particulate matter of size less than 10 and 2.5 micron(PM_(10) and PM_(2.5))and meteorological variables have been studied at a residential site near the coal-fired power plant in the two cities;Chandrapur and Nagpur during 2016–2019.O_(3) is observed to be not correlated significantly(rrandom forest model with O_(3) concentration as the response variable and NO_(2),NO,SO_(2),CO,BTEX,PM_(10) and PM_(2.5) as independent variables suggested that PM_(10),NO,CO and solar radiation are highly important variables governing the O_(3) dynamics in Chandrapur.In Nagpur,wind direction,relative humidity,temperature,toluene and NO_(2) are more important.Qualitative analysis to assess the contribution of emission sources suggested the influence of traffic emissions in Nagpur and the dominance of non-traffic related emissions,mainly power plant and mining activities in Chandrapur.The hazard quotient is observed to be>1 in both cities suggesting a health hazard to the residents living in the area.

关键词： Ground ozone Particulate matter Benzene,ethylbenzene,toluene and xylene(BTEX) Power plant Hazard quotient random forest model

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Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and random forest models

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Current Medical Science 2023年第5期43卷 908-918页

作者： Han-xiao WANG Xiao-zhao LIU Xi-miao HE Chao XIAO Dai-xin HUANG Shao-hua YI Department of Forensic Medicine Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Physiology School of Basic MedicineTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

关键词： body fluid identification mixture mixing ratio DNA methylation multiplex assay random forest model

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Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus

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World Journal of Diabetes 2023年第12期14卷 1793-1802页

作者： Hong-Miao Xu Xuan-Jiang Shen Jia Liu Department of Stomatology The First People’s Hospital of WenlingTaizhou 317500Zhejiang ProvinceChina Department of Stomatology Taizhou Central Hospital(Taizhou University Hospital)Taizhou 318000Zhejiang ProvinceChina

BACKGROUND Type 2 diabetes mellitus(T2DM)is associated with periodontitis.Currently,there are few studies proposing predictive models for periodontitis in patients with T2DM.AIM To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models.METHODS In this a retrospective study,300 patients with T2DM who were hospitalized at the First People’s Hospital of Wenling from January 2022 to June 2022 were selected for inclusion,and their data were collected from hospital records.We used logistic regression to analyze factors associated with periodontitis in patients with T2DM,and random forest and logistic regression prediction models were established.The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve(AUC).RESULTS Of 300 patients with T2DM,224 had periodontitis,with an incidence of 74.67%.Logistic regression analysis showed that age[odds ratio(OR)=1.047,95%confidence interval(CI):1.017-1.078],teeth brushing frequency(OR=4.303,95%CI:2.154-8.599),education level(OR=0.528,95%CI:0.348-0.800),glycosylated hemoglobin(HbA1c)(OR=2.545,95%CI:1.770-3.661),total cholesterol(TC)(OR=2.872,95%CI:1.725-4.781),and triglyceride(TG)(OR=3.306,95%CI:1.019-10.723)influenced the occurrence of periodontitis(Prandom forest model showed that the most influential variable was HbA1c followed by age,TC,TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showedthat in the training dataset, the AUC of the random forest model was higher than that of the logistic regressionmodel (AUC = 1.000 vs AUC = 0.851;P random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915;P > 0.05).CONCLUSION Both random forest and logistic regression models have good predictive value and can accurately predict the riskof periodontitis in patients with T2DM.

关键词： Type 2 diabetes mellitus Periodontitis Logistic regression Prediction model random forest model Gingival disease

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Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

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World Journal of Diabetes 2023年第10期14卷 1541-1550页

作者： Qing Lin Zhuan-Ji Fang Department of Obstetrics Fujian Maternity and Child Health HospitalCollege of Clinical Medicine for Obstetrics and Gynecology and PediatricsFujian Medical UniversityFuzhou 350001Fujian ProvinceChina

BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(Pmodel’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-p

关键词： Gestational diabetes mellitus Prediction model model evaluation random forest model Nomograms Risk factor

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Alterations in the human oral microbiome in cholangiocarcinoma

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Military Medical Research 2023年第5期10卷 726-729页

作者： Ben-Chen Rao Gui-Zhen Zhang Ya-Wen Zou Tong Ren Hong-Yan Ren Chao Liu Zu-Jiang Yu Zhi-Gang Ren Department of Infectious Diseases the First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052China Jinan Microecological Biomedicine Shandong Laboratory Jinan 250000China Gene Hospital of Henan Province Precision Medicine Centerthe First Affiliated Hospital of Zhengzhou Univer‑sityZhengzhou 450052China Department of Pharmacy the First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052China Shanghai Mobio Biomedical Technology Co. Ltd.Shanghai 201111China

Dear Editor,Alterations in the human microbiome are closely related to various hepatobiliary diseases.Gut microbial dysbiosis has been found in patients with cholangiocarcinoma(CCA)[1].However,the characteristics of o... 详细信息

关键词： Cholangiocarcinoma Oral microbiome Diagnostic biomarker random forest model 16S rRNA MiSeq sequencing

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An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet model

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Computers, Materials & Continua 2022年第4期71卷 629-649页

作者： Savita Khurana Gaurav Sharma Neha Miglani Aman Singh Abdullah Alharbi Wael Alosaimi Hashem Alyami Nitin Goyal Information Technology Department Seth Jai Parkash Mukand Lal Institute of Engineering and TechnologyRadaurHaryanaIndia Computer Science&Engineering Department Seth Jai Parkash Mukand Lal Institute of Engineering and TechnologyRadaurHaryanaIndia Computer Engineering Department National Institute of TechnologyKurukshetraHaryanaIndia Computer Science&Engineering Department Lovely Professional UniversityJalandharPunjabIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia

COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and random forest model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.

关键词： Covid-19 machine learning neuralprophet model poisson distribution prediction random forest model

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Regional variation of urban air quality in China and its dominant factors

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Journal of Geographical Sciences 2022年第5期32卷 853-872页

作者： ZHAO Yanyan ZHANG Xiaoping CHEN Mingxing GAO Shanshan LI Runkui College of Resources and Environment University of Chinese Academy of SciencesBeijing100049China Key Laboratory of Regional Sustainable Development modeling Institute of Geographic Sciences and Natural Resources ResearchCASBeijing100101China

It is of great theoretical and practical importance to carry out research on the spatio-temporal evolution of urban air pollution and its driving forces,which helps to facilitate a deeper understanding of the mutual feedback mechanisms between the urban environment and socio-economic systems.Comprehension of these mechanisms will contribute to the design and implementation of efficient environmental policies that ultimately will improve the quality of urbanization development.This paper illustrates the spatio-temporal evolutionary characteristics of six urban ambient air pollutant concentrations,namely,CO,NO_(2),O_(3),PM_(10),PM_(2.5),SO_(2),in 286 sample cities above the prefecture level in China from 2014 to 2019.The interactions between the pollutant concentrations are analyzed based on panel regression models.A random forest model is then employed to explore the correlations between the concentrations of these six pollutants and 13 natural and socio-economic impact factors to isolate the most crucial ones.The results reveal three aspects.First,within the research period,the average annual concentration of O_(3)increased while that of other pollutants decreased year by year.Second,there were significant interactions between concentrations of the six pollutants,leading to obvious compound air pollution in urban areas.Third,the impact of natural and socio-economic factors on urban air quality varied greatly among different air pollutants,with air temperature,vegetation coverage,urbanization level and traffic factors ranking high and the different response thresholds to the dominant influencing factors.In light of the limited ability of humans to control the natural environment and meteorological conditions,it is recommended that urban air quality be further improved by optimizing urban density,controlling anthropogenic emission sources,and implementing strict air pollution prevention and control measures.

关键词： urban air quality spatio-temporal evolution random forest model impact factor

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Dew amount and its long-term variation in the Kunes River Valley,Northwest China

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Journal of Arid Land 2022年第7期14卷 753-770页

作者： FENG Ting HUANG Farong ZHU Shuzhen BU Lingjie QI Zhiming LI Lanhai State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Tianshan Station for Snowcover and Avalanche Research Chinese Academy of SciencesXinyuan 835800China University of Chinese Academy of Sciences Beijing 100049China Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone Urumqi 830011China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi 830011China Department of Bioresource Engineering McGill UniversityMontreal H3A0G4Canada

Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),respectively.Four key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) century.It then significantly decreased for about a decade,after it increased slightly from 2013 to 2021.For the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and October.Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity.This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.

关键词： dew amount long-term variation meteorological variables random forest model multiple linear regression model Kunes River Valley

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