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Determining the key meteorological factors affecting atmospheric CO2 and CH4 using machine learning algorithms at a suburban site in China

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URBAN CLIMATE 2025年 59卷

作者： Liu, Wanyu Niu, Zhenchuan Feng, Xue Zhou, Weijian Liang, Dan Wang, Guowei Liu, Lin Beijing Normal Univ Interdisciplinary Res Ctr Earth Sci Frontier Beijing 100875 Peoples R China Chinese Acad Sci Inst Earth Environm State Key Lab Loess & Quaternary Geol Xian 710061 Peoples R China Xi An Jiao Tong Univ Inst Global Environm Change Xian 710049 Peoples R China Natl Observat & Res Stn Reg Ecol Environm Change & Xian Shaanxi Peoples R China Xian Inst Innovat Earth Environm Res Xian 710061 Peoples R China Joint Xian AMS Ctr IEECAS & Xian Jiaotong Univ Shaanxi Prov Key Lab Accelerator Mass Spectrometry Xian 710061 Peoples R China

Atmospheric CO2 and CH4 were measured from March 2023 to February 2024 at a suburban site near the northern foot of the Qinling Mountains, China, aiming to determine the key meteorological factors that influencing the seasonal CO2 and CH4 dynamics using machine learning (ML) algorithms. Yearly average atmospheric CO2 and CH4 were 446.1 +/- 10.0 ppm and 2118.9 +/- 50.5 ppb, respectively. Anthropogenic emissions dominated atmospheric CO2 and CH4 changes in winter, and the excess CO2 (Delta CO2) and CH4 (Delta CH4) above background levels during the heating period were mainly from combustion emissions. We selected seven ML algorithms to determine the variable importance of meteorological factors, among which eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) demonstrating the best performance and ranking consistency of the factors. Humidity and temperature of the atmosphere and soil had the higher effect (XGBoost: 80.4 %;RF: 78.1 %) on CO2 in spring, while humidity was crucial in winter, with variable importance of 69.3 % for XGBoost and RF. In summer and autumn, photosynthetic photon flux density and wind speed (WS) (totaling 35.2 % similar to 50.7 %) dominated CO2 dynamics. For CH4, atmospheric and soil humidity (totaling around 40.0 %) were key factors in spring, whereas atmospheric humidity was important in winter. WS had the largest effect in summer (XGBoost: 26.3 %;RF: 33.3 %) and autumn (XGBoost: 19.8 %;RF: 28.8 %). Meteorological processes like cold front passage significantly reduced CO2 and CH4 concentrations during haze events. XGBoost and RF have emerged as powerful tools for determining the key meteorological factor that favour seasonal GHGs evolution.

关键词： Dominant emission sources Variable importance of meteorological factor machine learning algorithms Random forest eXtreme gradient boosting

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Performance evaluation of different machine learning algorithms for prediction of nitrate in groundwater in Thiruvannamalai District

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CLEAN-SOIL AIR WATER 2025年第1期53卷

作者： Jacob, Christina Masilamani, Uma Shankar Vellore Inst Technol Sch Civil Engn Dept Environm & Water Resources Engn Vellore 632014 India

The prevalence of nitrates (NO3-) in groundwater due to the extensive application of fertilizers and anthropogenic sources pollutes the groundwater. machine learning (ML) techniques are now being increasingly deployed to achieve high precision in predicting water quality. This study assesses the efficacy of nine distinct ML algorithms, namely, linear regression, polynomial regression, decision tree, random forest (RF), support vector machine, multilayer perceptron regressor, eXtreme gradient boosting (XGB), light gradient boosting (LGB), and K-nearest neighbors to predict nitrate concentration in the groundwater in Thiruvannamalai District, Tamil Nadu. Overall, 360 water samples for 1 year and 14 groundwater variables were determined to predict nitrate. Performance evaluation metrics such as root mean square error (RMSE), moving average error (MAE), and correlation coefficient (R2) were evaluated for pre-monsoon, monsoon, and post-monsoon seasons. For all three seasons, RF predicted the nitrate concentration with low values of RMSE, MAE, and higher values of R2. The results show values for RF with: RSME: 0.49, MAE: 1.30, and R2: 0.94, which has a higher prediction tailed by LGB and XGB and is true for all the seasons. The results from the study will aid the policymakers in planning the strategy for remediation. Graphical Abstract: Main sources of nitrate contamination are agricultural fertilizers, industrial discharges, septic tanks, landfills, and animal feedlots. GIS was used to analyze the spatial distribution for each of the three seasons, pre-monsoon, monsoon, and post-monsoon, in Polur Taluk. Nine distinct machine learning algorithms were evaluated to predict nitrate and are presented in a scatterplot. image

关键词： groundwater machine learning algorithms nitrate contamination prediction RMSE

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Prediction of late-onset depression in the elderly Korean population using machine learning algorithms

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Park, Jong Wan Ko, Chang Woo Lee, Diane Youngmi Kim, Jae Chul Hannam Univ Grad Sch Dept Counseling 70 Hannam Ro Daejeon 34430 South Korea Seoul Natl Univ Coll Med Dept Biomed Sci 103 Daehak Ro Seoul 03080 South Korea Daegu Cyber Univ Dept Art Therapy 201 Daegudae Ro Gyongsan 38453 South Korea

Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey 'Korean Longitudinal Study of Aging,' we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, Random Forest Classifier and Gradient Boosting Classifier demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services.

关键词： Late-onset depression Longitudinal study of aging Depression trajectories machine learning algorithms Predictive performance

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An early prediction model for gestational diabetes mellitus created using machine learning algorithms

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INTERNATIONAL JOURNAL OF GYNECOLOGY & OBSTETRICS 2025年第0期 2025 Mar 4页

作者： Yang, Zhifen Shi, Xiaoyue Wang, Shengpu Du, Lijia Zhang, Xiaoying Zhang, Kun Zhang, Yongqiang Ma, Jinlong Zheng, Rui Hebei Med Univ Hosp 4 Obstetr Dept 169 Tianshan St Shijiazhuang 050000 Peoples R China Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang Hebei Peoples R China

ObjectiveTo investigate high-risk factors for gestational diabetes mellitus (GDM) in early pregnancy through an analysis of demographic and clinical data, and to develops a machine-learning-based prediction model to enhance early diagnosis and intervention. MethodsA retrospective study was performed involving 942 pregnant women. A stacking ensemble (machine learning [ML]) was applied to demographic and clinical variables, creating a predictive model for GDM. Model performance was evaluated through receiver-operating characteristics (ROC) analysis, and the area under the curve (AUC) was calculated. Risk stratification was performed using quartile-based probability thresholds, and predictive accuracy was validated using an independent dataset. ResultsSignificant predictors for GDM included age, pre-pregnancy body mass index (BMI;calculated as weight in kilograms divided by the square of height in meters), history of GDM, family history of diabetes, history of fetal macrosomia, education level, history of hypertension, and gravidity. These factors, which can be collected non-invasively at the first prenatal visit, formed the basis of a robust predictive model (AUC = 0.89). The model demonstrated a strong ability to exclude GDM, at a threshold of 28.53%. ConclusionsThe machine-learning-based prediction model effectively identifies populations at high risk for GDM before invasive testing and oral glucose tolerance test, facilitating early clinical intervention and resource optimization.

关键词： early prediction model gestational diabetes mellitus hierarchical structural analysis machine learning algorithms

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A novel high-frequency cutting force compensation model for micro-milling polyvinylidene fluoride thin films tri-axis force sensors based on machine learning algorithms

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PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2025年 95卷 188-202页

作者： Huang, Haizhong Zhao, Honghao Zhang, Chongtian Wang, Yanshen Liu, Huiying Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Peoples R China Harbin Inst Technol Weihai Dept Mech Engn Weihai 264209 Peoples R China Tsinghua Univ State Key Lab Tribol Adv Equipment Beijing 100084 Peoples R China

During micro-milling processes, real-time high-precision monitoring of cutting forces is crucial. In this study, a high-frequency micro cutting force sensor based on polyvinylidene fluoride thin films was developed. The data obtained from the micro cutting force sensor were compared with actual cutting force measurements, leading to the construction of a cutting force compensation model utilizing three machine learning algorithms: extreme gradient boosting, least squares boosting, and gaussian kernel regression. A comparative analysis of these three algorithms revealed that the gaussian kernel Regression algorithm excels in accurately constructing the cutting force compensation model, while the least squares boosting algorithm demonstrates substantial robustness. This study provides a method for constructing a cutting force signal compensation model and offers a new intelligent monitoring approach for subsequent research.

关键词： Polyvinylidene fluoride thin films machine learning algorithms Cutting force Compensation model

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Predicting host-pathogen interactions with machine learning algorithms: A scoping review

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INFECTION GENETICS AND EVOLUTION 2025年 130卷 105751页

作者： Sahragard, Rasool Arabfard, Masoud Najafi, Ali Baqiyatallah Univ Med Sci Biomed Technol Inst Mol Biol Res Ctr Tehran Iran Baqiyatallah Univ Med Sci Syst Biol & Poisonings Inst Chem Injuries Res Ctr Tehran Iran

Background: Diseases caused by pathogenic microorganisms pose a persistent global health challenge. Pathogens exploit host mechanisms through intricate molecular interactions. Understanding these host-pathogen interactions (HPIs), particularly protein-protein interactions (PPIs), is crucial for developing therapeutic strategies. While experimental approaches are essential, they are often labor-intensive and costly. Researchers have been able to predict HPIs more efficiently due to recent advances in artificial intelligence and machine learning. However, existing reviews lack a systematic evaluation of different machine learning methodologies and their effectiveness. Methods: This scoping review critically examines recent studies on machine learning-based Host-Pathogen Interaction (HPI) prediction, categorizing them by host and pathogen types, machine learning algorithms, and key evaluation metrics. The methodology is based on the study beginning with a preliminary search in reputable using key phrases related to host-pathogen interactions from 2019 to 2024. This process yielded 46 relevant articles, from which 30 were selected for review after evaluating titles and abstracts. Results: Our findings indicate that tree-based algorithms, particularly Random Forest and Gradient Boosting, are the most prevalent in Host-Pathogen Interaction (HPI) prediction. The filter articles were categorized by host and pathogen type and further subdivided into four subcategories based on the prediction type and machine learning algorithms: classic, tree-based, vector-based, and neural network algorithms. Convolutional and recurrent neural networks are among the deep learning models that demonstrate promising accuracy, but they require a lot of labeled data for effective training. Additionally, the analysis uncovers significant gaps in dataset standardization and model interpretability, which pose challenges to the broader applicability of these predictive models. Conclusion: In this re

关键词： Host-pathogen interactions Protein-protein interaction Prediction algorithms machine learning algorithms Bioinformatics

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Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations

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SCIENTIFIC REPORTS 2025年第1期15卷 1-10页

作者： Li, Yong Sun, Xin Qu, Yongtao Yang, Shuling Zhai, Yueyi Qu, Yan Hebei Med Univ Dept Otolaryngol Shijiazhuang Peoples R China Hebei Gen Hosp Dept Otolaryngol Shijiazhuang Peoples R China Hebei North Univ Zhangjiakou Peoples R China Hebei Med Univ Anim Lab Hosp 3 Shijiazhuang Peoples R China Hebei Med Univ Dept Otolaryngol Hosp 3 Shijiazhuang Peoples R China

This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induced hearing loss (ONIHL)) at Hebei Medical Examination Center (2017-2023) were analyzed. Workers with combined exposure and without specific contraindications or other hearing impairment causes were included. Demographic and clinical data were gathered. Chi-square and Mann-Whitney U tests examined variables, and multivariate logistic regression determined ONIHL risk factors. machine learning algorithms like Logistic Regression and Random Forest were developed, optimized, and evaluated. Results showed significant differences in gender, exposure, blood pressure, smoking, etc. between ONIHL and non-ONIHL groups. Male gender, combined exposure, diastolic blood pressure elevation, smoking, fasting blood glucose elevation, and age were positive predictors, while systolic blood pressure elevation was negative. The logistic model had the highest predictive ability (ROC = 0.714). Subgroup analysis revealed a significant positive correlation in specific subgroups. In summary, combined exposure increased ONIHL risk and affected health. machine learning effectively predicted ONIHL, but the study had limitations and needed further research.

关键词： Occupational exposure Noise and dust Hearing loss machine learning algorithms Risk factors

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Rainfall Classification Using machine learning algorithms on Data Mining Platforms

IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING

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IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2025年第2期48卷 109-114页

作者： Turk, Sevtap Istanbul Univ Cerrahpasa Vocat Sch Tech Sci TR-34500 Istanbul Turkiye

Weather conditions directly affect sectors such as agriculture and transport. With climate change, unpredictability is increasing and traditional calculation methods may not be sufficient. In addition to some statistical methods, machine learning algorithms are also used for weather forecasting. This study attempts to classify precipitation using machine learning algorithms on selected meteorological data. The models used are K-nearest neighbors (KNNs), support vector machine (SVM), and multilayer perceptron (MLP). These models were implemented on four different open-source and free data mining platforms. These platforms are Altair AI Studio (formerly Rapidminer), Knime, Orange, and Weka. The dataset includes parameters such as pressure, temperature, humidity, number of rainy days, cloudiness rate, and year and month information. According to the values of these parameters, the data were classified as less rainy, rainy, and very rainy.

关键词： Support vector machines Nearest neighbor methods machine learning algorithms Classification algorithms Rain Data mining Weather forecasting Prediction algorithms Training Humidity Classification machine learning algorithms open-source data mining software weather forecast

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using machine learning algorithms

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model machine learning algorithms Remote sensing images

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Prediction models based on machine learning algorithms for COVID-19 severity risk

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BMC PUBLIC HEALTH 2025年第1期25卷 1-19页

作者： Zhang, Hansong Wang, Ying Xie, Yan Wang, Cuihan Ma, Yuqi Jin, Xin Tianjin Univ Sch Mech Engn Tianjin 300350 Peoples R China Tianjin First Ctr Hosp Dept Nursing Tianjin 300196 Peoples R China Tianjin First Ctr Hosp Dept Liver Transplantat Tianjin 300196 Peoples R China Tianjin Med Univ Tianjin Nankai Hosp Tianjin 300000 Peoples R China Tianjin Key Lab Acute Abdomen Dis Associated Organ Tianjin 300000 Peoples R China Inst Integrat Med Acute Abdominal Dis Tianjin 300000 Peoples R China Tianjin Univ Med Sch Tianjin 300072 Peoples R China Tianjin Municipal Hlth Commiss Tianjin 300070 Peoples R China

BackgroundThe World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X;therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar *** models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. These models were evaluated for prediction accuracy, area under the curve (AUC), sensitivity, and specificity as well as were interpreted using SHapley Additive *** were collected from 1,485 hospitalized patients across 6 centers, comprising 1,184 patients with severe or critical COVID-19 and 301 patients with nonsevere COVID-19. Among the four models, the SVM model achieved the highest prediction accuracy of 98.45%, with an AUC of 0.994, a sensitivity of 0.989, and a specificity of 0.969. Moreover, oxygenation index (OI), confusion, respiratory rate, and age were found to be predictors of COVID-19 severity *** could accurately predict COVID-19 severity risk;thus, it can be prioritized as a prediction model. OI is the most critical predictor of COVID-19 severity risk and can serve as the primary and independent evaluation indicator.

关键词： Prediction models machine learning algorithms Severity risk COVID-19

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