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Environmental variables controlling soil organic carbon in top- and sub-soils in karst region of southwestern China

ECOLOGICAL INDICATORS 2018年 90卷 624-632页

作者： Zhu, Jie Wu, Wei Liu, Hong-Bin Southwest Univ Coll Resources & Environm Chongqing 400716 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400716 Peoples R China

Detailed information about the variability of soil organic carbon (SOC) content in different horizons is vital for the effective management of soil fertility and can improve understanding of SOC accumulation and decomposition. However, few studies have explored the variations of SOC content of cultivated lands in different horizons at the regional scale, particularly in a karst region in southwestern China. In this work, 235 soil samples were collected from the A to C horizons at 80 sites over the study area. Classical statistics and classification and regression trees (CARTs) were applied to investigate the critical environmental variables (topography, climate and parent material) that control SOC content variability in various horizons. Results indicated that SOC content decreased and its variability increased with an increase in horizon depth. The SOC content in the three horizons was predicted well by the CART models. The mean absolute prediction error, root mean square error and coefficient of determination ranged from 2.38 to 3.85 g kg(-1), 3.23 to 5.2 g kg(-1) and 0.5837 to 0.856, respectively. The importance of the environmental variables in SOC content varied with the horizons. In the A horizon, topographical variables, such as terrain wetness index (TWI), terrain ruggedness index and slope, were the key factors affecting SOC content variability. In the B horizon, the topography still showed the primary influence by elevation and TWI, meanwhile the importance of the parent material strengthened. Climate variables had the greatest impact on SOC content in the C horizon. TWI and precipitation that directly influenced soil moisture were the primary factors controlling SOC content in the three horizons.

关键词： Soil organic carbon content Soil horizon Environmental variable classification and regression tree Importance

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An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

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Pedosphere 2018年第5期28卷 739-750页

作者： ZHI Junjun ZHANG Ganlin YANG Renmin YANG Fei JIN Chengwei LIU Feng SONG Xiaodong ZHAO Yuguo LI Decheng State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing 210008(China) University of Chinese Academy of Sciences Beijing 100049 (China)

Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely ***, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.

关键词： boosted regression trees classification and regression tree digital soil mapping random forest soil diagnostic horizons,support vector machine

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Low Flows Regionalization in North-Western Italy

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WATER RESOURCES MANAGEMENT 2010年第14期24卷 4049-4074页

作者： Vezza, Paolo Comoglio, Claudio Rosso, Maurizio Viglione, Alberto Politecn Torino Dipartimento Idraul Trasporti & Infrastrutture I-10129 Turin Italy Politecn Torino Dipartimento Territorio Ambiente & Geotecnol I-10129 Turin Italy Tech Univ Wien Inst Wasserbau & Ingn Hydrol A-1040 Vienna Austria

Prediction of low flows in ungauged catchments is needed in many branches of water resources management, including water availability and river ecology studies. In this paper we analyze the regional variability of q (95), i.e., the specific discharge that is exceeded 95% of the time, in North-Western Italy (Piemonte and Valle d'Aosta Regions). Multiple regressions with morphoclimatic catchment characteristics are applied in subregions obtained through four classification methods: Seasonality Indices (SI), classification and regression trees (CRT), Residual Pattern Approach (RPA) and Weighted Cluster Analysis (WCA). All the classification methods separate the South-Eastern Apennine-Mediterranean area from the rest of the study domain (the Alps mountain range), even if they use different criteria to carry out this division (e.g., the percentage of forest, seasonality of low flows, combination of several parameters). In the Apennine-Mediterranean part of the area, low flows occur in summer with a long period of drought and are mainly due to dry climate, moderate snowpack storage and high evapotranspiration. In Alpine catchments low flows occur in winter and vary according to precipitation, elevation, interactions with aquifers and land cover. Within the Alpine mountain range the CRT algorithm identifies a number of small high-elevation catchments in which the intense drought period during winter has the soil freezing processes as the driving force. From a statistical point of view, the CRT model outperforms the models obtained by the other classification techniques in terms of explained variance (69%). Because of this, and given the meaningful hydrological interpretation of the results, we use the CRT model for the regionalization of q (95) in Piemonte and Valle d'Aosta. Lastly, as operational procedure for future low flow regionalization studies, we suggest that more classification methods should be applied to assist the critical analysis of the results.

关键词： Low flows Regionalization Regional regression classification and regression tree CART

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Sensitivity analysis and ensemble artificial intelligence-based model for short-term prediction of NO2 concentration

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2021年第9期18卷 2703-2722页

作者： Nourani, V. Abdollahi, Z. Sharghi, E. Univ Tabriz Ctr Excellence Hydroinformat Tabriz Iran Univ Tabriz Fac Civil Engn Tabriz Iran Near East Univ Fac Civil & Environm Engn Via Mersin 10 TR-99138 Nicosia N Cyprus Turkey

In this study, in the first step, three scenarios with different input combinations are created to implement a sensitivity analysis for hourly NO2 prediction in Columbus City, Ohio. Three classes of inputs including concentration-related data (NO2 concentration at previous time steps and NO2 concentration in the suburban monitoring station), meteorology (wind speed, wind direction, and temperature), and traffic-related data (traffic count, hour of the day, and day of the week) are applied to create three scenarios. Also, the support vector regression methodology is employed to perform the sensitivity analysis. Dominant variables determined in the sensitivity analysis are applied as inputs to three models called feed-forward neural network, support vector regression, as well as classification and regression tree. In the last step, ensemble techniques including simple linear averaging, weighted linear averaging, and nonlinear support vector regression ensemble are proposed to improve the performance of sole models. The results indicate that, in the urban area, in addition to NO2 variations in the previous time step, other variables such as hourly traffic count in freeway loop, suburban NO2 concentration, and hour of the day can affect the NO2 concentration. Further, the values of determination coefficient for the individual models, namely classification and regression tree and feed-forward neural network, are 67 and 81% that the ensemble technique as a post-processing approach enhances the performance of them up to 19% and 5% in the verification steps, respectively.

关键词： Air pollution modelling Traffic-related pollutant Support vector regression Feed-forward neural network classification and regression tree Columbus City

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Medium- and long-term runoff forecasting based on a random forest regression model

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WATER SUPPLY 2020年第8期20卷 3658-3664页

作者： Chen Shijun Wei Qin Zhu Yanmei Ma Guangwen Han Xiaoyan Wang Liang Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Business Sch Chengdu 610065 Peoples R China State Grid Sichuan Elect Power Co Chengdu 610041 Peoples R China

Medium- and long-term runoff forecasting is closely related to the generation capacity forecasting of cascade hydropower stations, which is of great significance to power plants when arranging production plans and assisting market decisions. In order to improve the accuracy of runoff forecasting, an attempt was made to use random forest regression (RFR) to model the medium- and long-term runoff forecasting and to further make a verification based on the actual monthly runoff data of Mupo and Chuntangba stations. By comparison with the forecast results attained through a support vector machine (SVM) and an integrated autoregressive moving average model (IARMA), the results showed that the RFR model had the lowest mean square error (MSE) among the three methods. In addition, the coefficients of determination R-2 of the RFR for the two stations increased by 0.0261 and 0.0295 compared with the SVM model, and the R-2 rose by 0.1134 and 0.1332 compared with the IARMA model. The comparison of the three methods showed that the RFR had higher forecasting accuracy as well as stronger reliability and practicability than the IARMA model and the SVM model, so the RFR provided a new idea and method for the study of runoff forecasting.

关键词： Bagging algorithm classification and regression tree mean square error random forest regression model runoff forecasting

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Influence of AOD remotely sensed products, meteorological parameters, and AOD-PM_2.5 models on the PM_2.5 estimation

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2021年第4期35卷 893-908页

作者： Xu, Yuelei Huang, Yan Guo, Zhongyang East China Normal Univ Minist Educ Key Lab Geog Informat Sci 500 Dongchuan Rd Shanghai 200241 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China

Air pollution has emerged as one of the most urgent problems worldwide over the past few years. Especially, fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) has seriously endangered the human life and health worldwide. Thus, monitoring accurate and spatio-temporal continuously PM2.5 concentrations is essential for controlling air pollution. The key to accurately estimating PM2.5 concentrations is to build a reliable aerosol optical depth (AOD)-PM2.5 model, which is mainly determined by the AOD remotely sensed products, meteorological parameters, and estimating methods. However, no systematic evaluation and analysis of the influence of these three variables on AOD-PM2.5 model has yet been reported. Based on Terra and Aqua Moderate Resolution Imaging Spectroradiometer AOD products, reanalysis meteorological data, and ground-based PM2.5 data during 2014-2015 in eastern China, we analyzed the influence of above mentioned variables on the accuracy of the AOD-PM2.5 model. The four modeling methods investigated are stepwise multiple linear regression (SMLR), backpropagation neural network (BPNN), classification and regression tree (CART), and random forest (RF). Adjusted R-2, mean absolute error (MAE), product index (PI), spatial resolution index (SRI), and Taylor diagrams are used to assess the accuracy of models. The results show that adding meteorological elements that vary with time and height can improve the accuracy by 4.97% over eastern China. The average PI of Terra AOD products increased by 0.15 compared with that of Aqua AOD products. Most SRIs have positive values, suggesting that AOD products with a spatial resolution of 10 km can lead to a more accurate AOD-PM2.5 model. The Taylor diagram indicates that RF model > BPNN model > CART model > SMLR model in terms of performance. Therefore, Terra AOD products with spatial resolution of 10 km and RF are more suitable for use with the AOD-PM2.5 model in eastern China.

关键词： MODIS AOD products Stepwise multiple linear regression Backpropagation neural network classification and regression tree Random forest Taylor diagram

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An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

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IEEE ACCESS 2024年 12卷 41773-41786页

作者： Qiu, Junping Han, Xiaolin Hangzhou Dianzi Univ Sch Management Hangzhou 310018 Peoples R China

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

关键词： Academic papers multiple linear regression artificial neural network classification and regression tree prediction model research value

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Developing radiology diagnostic tools for pulmonary fibrosis using machine learning methods

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CLINICAL IMAGING 2024年 106卷 110047-110047页

作者： Fan, Weijia Chen, Qixuan Maccarrone, Valerie Luk, Lyndon Navot, Benjamin Salvatore, Mary Columbia Univ Mailman Sch Publ Hlth Dept Biostat 722st 168th St New York NY 10032 USA Columbia Univ Dept Radiol Irving Med Ctr 630 W 168th St New York NY 10032 USA

Background: Accurate and prompt diagnosis of the different patterns for pulmonary fibrosis is essential for patient management. However, accurate diagnosis of the specific pattern is challenging due to overlapping radiographic characteristics. Materials and methods: We conducted a retrospective chart review utilizing two machine learning methods, classification and regression tree and Bayesian additive regression tree, to select the most important radiographic features for diagnosing the three most common fibrosis patterns and created an online diagnostic app for convenient implementation. Results: Four hundred patients (median age of 67 with inter quartile range 58-73;200 males) were included in the study. Peripheral distribution, homogeneity, lower lobe predominance and mosaic attenuation of fibrosis are the four most important features identified. Bayesian additive regression tree demonstrates better performance than classification and regression tree in diagnosis prediction and provides the predicted probability of each diagnosis with uncertainty intervals for each combination of features. Conclusion: The model and app built with Bayesian additive regression tree can be used as an effective tool in assisting radiologists in the diagnostic process of pulmonary fibrosis pattern recognition.

关键词： Pulmonary fibrosis Machine learning classification and regression tree Bayesian additive regression tree Diagnostic tool Online implementation tool

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Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第1期63卷 51-59页

作者： Mohktar, Mas S. Redmond, Stephen J. Antoniades, Nick C. Rochford, Peter D. Pretto, Jeffrey J. Basilakis, Jim Lovell, Nigel H. McDonald, Christine F. Univ New S Wales Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ Malaya Dept Biomed Engn Fac Engn Kuala Lumpur 50603 Malaysia Austin Hlth Dept Resp Med Inst Breathing & Sleep Heidelberg Vic 3081 Australia John Hunter Hosp Dept Resp Med Newcastle NSW 2305 Australia Univ Western Sydney Sch Comp & Math Sydney NSW 2751 Australia

Background: The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission. Objective: The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict. the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month. Method: The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit. Results: The CART algorithm can classify home telehealth measurement data into either a 'low risk' or 'high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a 'high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records. Conclusion: The CART analyses have shown that features extracted from three types of physiological measurements;forced expiratory volume in 1 s (FEV1), arterial oxygen saturation (SPO2) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth dat

关键词： Home telehealth system classification and regression tree Chronic obstructive pulmonary disease (COPD)

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Use of a big data analysis technique for extracting HRA data from event investigation reports based on the Safety-II concept

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 194卷 106232-000页

作者： Ham, Dong-Han Park, Jinkyun Chonnam Natl Univ 77 Yongbong Ro Gwangju 61186 South Korea KAERI 1045 Daedeokdaero Daejeon 34057 South Korea

The safe operation of complex socio-technical systems including NPPs (Nuclear Power Plants) is a determinant for ensuring their sustainability. From this concern, it should be emphasized that a large portion of safety significant events were directly and/or indirectly caused by human errors. This means that the role of an HRA (Human Reliability Analysis) is critical because one of its applications is to systematically distinguish error-prone tasks triggering safety significant events. To this end, it is very important for HRA practitioners to access diverse HRA data which are helpful for understanding how and why human errors have occurred. In this study, a novel approach is suggested based on the Safety-II concept, which allows us to collect HRA data by considering failure and success cases in parallel. In addition, since huge amount of information can be gathered if the failure and success cases are simultaneously involved, a big data analysis technique called the CART (classification and regression tree) is applied to deal with this problem. As a result, it seems that the novel approach proposed by combining the Safety-II concept with the CART technique is useful because HRA practitioners are able to get HRA data with respect to diverse task contexts.

关键词： Human reliability analysis Nuclear power plant Safety-II classification and regression tree Event investigation report

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