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Functional characterization of current characteristic of direct methanol fuel cell

FUEL 2016年 183卷 432-440页

作者： Garg, A. Panda, Biranchi Narayan Lam, Jasmine Siu Lee Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Univ Lisbon Inst Super Tecn IDMEC Ave Rovisco Pais P-1040001 Lisbon Portugal

In the last few years, direct methanol fuel cells (DmFCs) are extensively considered as a viable alternative power source to batteries used in transportation and portable devices due to their high energy density properties. Despite these significant advantages, operational and technological improvements are still required to make them a cost effective process. Past studies reveal that experimental procedures were mainly used in optimizing the performance of DmFC systems. Alternatively, mathematical modelling can be a promising way of finding the best operating conditions for improving the performance of DmFC systems because it is a cost effective approach and also requires less time for its implementation. Therefore, the present work proposes two artificial intelligence methods (Gene Expression Programming (GEP) and m5 model tree) to study the current characteristic of fuel cell with respect to five input operating conditions of DmFC. Performance of the two methods is evaluated against the actual data based on the three statistical metrics, hypothesis tests and cross-validation procedure. The hidden relationships between the fuel cell current and the five operating conditions are extracted by performing a parametric analysis on the model. It is found that the cell temperature has the highest impact on the current characteristic of DmFCs. The insightful information extracted from the model analysis could be useful for improving the working efficiency of the fuel cell. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Direct methanol fuel cell DFmC Fuel cell performance Gene expression Programming m5 model tree

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Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran

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ENVIRONmENTAL SCIENCE AND POLLUTION RESEARCH 2018年第5期25卷 4776-4786页

作者： Sattari, mohammad Taghi Farkhondeh, Arya Abraham, John Patrick Univ Tabriz Dept Water Engn 29 Bahman Blvd Tabriz East Azerbaijan Iran Univ St Thomas Sch Engn 2115 Summit Ave St Paul MN USA

Water quality is a major concern around the world, particularly in dry climates. Usually, assessment of surface water quality is costly and time-consuming. In this situation, a method which could estimate the water quality accurately with the minimum of hydro-chemical parameters would be appealing. In this study, three data mining methods, namely, m5 model tree, support vector machine (SVm), and Gaussian process (GP), were employed to estimate the sodium adsorption ratio (SAR) indicator in the Shahrchay River located in the west of the Urmia Lake basin, Iran. Results from these methods were compared with an artificial neural network (ANN). Different hydro-chemical parameters were assessed and the most effective parameters were selected. Five combinations of the selected parameters were developed as input parameters to the models. The results indicated that the m5 model tree has a superior performance among the data mining methods, where the combination of sodium and electrical conductivity (Na and EC) is used as input parameters, with a coefficient of determination (R-2) = 0.987, root mean squared error (RmSE) = 0.017, mean absolute error (mAE) = 0.012, and mean relative error (mRE) = 5.584. Also, a sensitivity analysis was carried out which reported that the SAR is more sensitive to Na, Ca, and EC, respectively. This research highlights that the m5 model tree can be successfully employed for the estimation of SAR. It also indicates that the practical and simple linear equations and optimization performed with the m5 model tree reduce time and cost.

关键词： Water quality Sodium adsorption ratio m5 model tree Support vector machine (SVm) Gaussian process (GP) Urmia Lake basin

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Evaporation modelling by heuristic regression approaches using only temperature data

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2019年第6期64卷 653-672页

作者： Kisi, Ozgur Heddam, Salim Ilia State Univ Fac Nat Sci & Engn Tbilisi Georgia Univ Skikda Fac Sci Dept Agron Skikda Algeria

Accurate estimation of pan evaporation (E-pan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (mARS) and m5 model tree (m5tree) in estimating pan evaporation using only temperature data as input. monthly minimum temperature, maximum temperature and E-pan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%-50%, 60%-40% and 75%-25%) were employed for better evaluation of the applied methods. The results show that the mARS method generally estimated monthly E-pan with higher accuracy compared to the m5tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.

关键词： pan evaporation estimation mARS m5 model tree Stephens-Stewart

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Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms

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ENGINEERING APPLICATIONS OF COmPUTATIONAL FLUID mECHANICS 2020年第1期14卷 311-322页

作者： Kargar, Katayoun Samadianfard, Saeed Parsa, Javad Nabipour, Narjes Shamshirband, Shahaboddin mosavi, Amir Chau, Kwok-wing Urmia Univ Dept Civil Engn Orumiyeh Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ton Duc Thang Univ Dept Management Sci & Technol Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City Vietnam Obuda Univ Kalman Kando Fac Elect Engn Budapest Hungary Oxford Brookes Univ Sch Built Environm Oxford England J Selye Univ Dept Math & Informat Komarno Slovakia Bauhaus Univ Weimar Inst Struct Mech Weimar Germany Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, m5 model tree (m5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RmSE) and mean absolute error (mAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, m5P had superior performance, with CC of 0.823, RmSE of 454.9 and mAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RmSE of 460.7 and mAE of 306.1, gave more precise results than the other empirical models. The main advantage of m5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed m5P model with simple formulations was superior to other machine learning models and empirical models;therefore, it can be used as a proper tool for estimating the LDC in rivers.

关键词： Gaussian process regression longitudinal dispersion coefficient m5 model tree random forest rivers support vector regression

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Predicting Standardized Streamflow index for hydrological drought using machine learning models

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ENGINEERING APPLICATIONS OF COmPUTATIONAL FLUID mECHANICS 2020年第1期14卷 339-350页

作者： Shamshirband, Shahabbodin Hashemi, Sajjad Salimi, Hana Samadianfard, Saeed Asadi, Esmaeil Shadkani, Sadra Kargar, Katayoun mosavi, Amir Nabipour, Narjes Chau, Kwok-Wing Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh Vietnam Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh Vietnam Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Urmia Univ Fac Engn Dept Civil Engn Orumiyeh Iran Obuda Univ Kalman Kando Fac Elect Engn Budapest Hungary Oxford Brookes Univ Sch Built Environm Oxford England Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Hong Kong Peoples R China

Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and m5 model trees (mT). The results indicate that SPI delivered higher accuracy. moreover, mT model performed better in predicting SSI by a CC of 0.8195 and a RmSE of 0.8186.

关键词： Gene expression Programming hydrological drought m5 model tree machine learning models Standardized Streamflow index support vector regression

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Advanced machine learning models development for suspended sediment prediction: comparative analysis study

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GEOCARTO INTERNATIONAL 2022年第21期37卷 6116-6140页

作者： Achite, mohammed Yaseen, Zaher mundher Heddam, Salim malik, Anurag Kisi, Ozgur Univ Hassiba Benbouali Chlef Fac Nat & Life Sci Lab Water & Environm Chlef Algeria Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar Iraq Hydraul Div Univ Fac Sci Agron Dept Route El Hadaik Skikda Algeria Punjab Agr Univ Reg Res Stn Bathinda Punjab India Ilia State Univ Sch Technol Civil Engn Dept Tbilisi Georgia

Accurate estimation of suspended sediment (SS) is very essential for planning and management of hydraulic structures. The study investigates the accuracy of four machine learning methods, dynamic evolving neural-fuzzy inference systems (DENFIS), fuzzy c-means based adaptive neuro fuzzy system (ANFIS-FCm), multivariate adaptive regression spline (mARS) and m5 model tree (m5tree), in estimating suspended sediments. Several input scenarios including streamflow (Q) and sediment (S) data obtained from Ain Hamara Station in Wadi Abd basin, Algeria were constructed to find the most effective one. The research results indicate that the DENFIS model with current streamflow (Qt) and 1 previous sediment (St-1) values performs superior to the other alternatives in SS estimation;it increases the efficiency of the best ANFIS-FCm, mARS and m5tree by 1.6%, 15.7% and 9.6% with respect to RmSE (root mean square error), respectively. Variation of Q and S data on models' estimation ability was also investigated and it was found that the variation input considerably increase the prediction ability of mARS method;increments in RmSE and mAE (mean absolute error) are by 10.8 and 4.9% and decrement in NSE (Nash-Sutcliffe efficiency) is by 12.9%.

关键词： mapping streamflow-sediment relationship DENFIS ANFIS-FCm mARS m5 model tree

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Evaluation of SVm, ELm and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China

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AGRICULTURAL AND FOREST mETEOROLOGY 2018年 263卷 225-241页

作者： Fan, Junliang Yue, Wenjun Wu, Lifeng Zhang, Fucang Cai, Huanjie Wang, Xiukang Lu, Xianghui Xiang, Youzhen Northwest A&F Univ Minist Educ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Yangling 712100 Shaanxi Peoples R China Zhejiang Univ Water Resources & Elect Power Coll Water Resources & Environm Engn Hangzhou 310018 Zhejiang Peoples R China Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China Yanan Univ Coll Life Sci Yanan 716000 Peoples R China

Accurate estimation of reference evapotranspiration (ET0) is of great importance for the regional water resources planning and irrigation scheduling design. The FAO-56 Penman-monteith model is recommended as the reference model to predict ET0, but its application is commonly restricted by lack of complete meteorological data at many worldwide locations. This study evaluated the potential of machine learning models, particularly four relatively simple tree-based assemble algorithms (Le. random forest (RF), m5 model tree (m5tree), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost)), for estimating daily ET0 with limited meteorological data using a K-fold cross-validation method. For assessment of the tree-based models in terms of prediction accuracy, stability and computational costs, these models were further compared with their corresponding support vector machine (SVm) and extreme learning machine (ELm) models. Four input combinations of daily maximum and maximum temperature (T-max and T-min), relative humidity (H-r), wind speed (U-2), global and extra-terrestrial solar radiation (R-s and R-a) with T-max, T-min and R-a as the base dataset were considered using meteorological data during 1961-2010 from eight representative weather stations in different climates of China. The results showed that, when lack of complete meteorological data, the machine learning models using T-max, T-min, H-r, U-2 and R-a obtained satisfactory ET0 estimates in the temperate continental, mountain plateau and temperate monsoon zones of China (RmSE < 0.5 mm d(-1)). However, models with three input parameters of T-max, T-min and R-s were superior for daily ET0 prediction in the tropical and subtropical zones. The ELm and SVm models offered the best combination of prediction accuracy and stability. The simple tree-based XGBoost and GBDT models showed comparable accuracy and stability to the SVm and ELm models, but exhibited much less computational costs. Consideri

关键词： Reference evapotranspiration Random forest m5 model tree Gradient boosting decision tree Extreme gradient boosting

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User Behavior Analysis Based on User Interest by Web Log mining 27

User Behavior Analysis Based on User Interest by Web Log Min...

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27th International Telecommunication Networks and Applications Conference (ITNAC)

作者： Luo, Xipei Wang, Jing Shen, Qiwei Wang, Jingyu Qi, Qi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China EBUPT Informat Technol Co Ltd Beijing 100191 Peoples R China

ISBN: (纸本)9781509067961

With the rapid development of science and technology and the growing popularity of computer networks, the scale of network users is gradually expanding, and the behavior of network users is becoming more and more complicated. A large number of studies show that the user's actual interest is closely related to the browsing behavior on the web page. Through the user browsing behavior analysis can obtain the user interest information, and then build the user interest model, so that the search results closer to the user's expectations. This paper mainly introduces the method of web log mining, which can discover the mode of web pages by digging web log records. By analyzing and exploring the rules of web log records, we can identify the potential customers of the website and improve the quality of information services to users. In the stage of user behavior analysis, this paper explores the differences in user browsing behavior in different types of access events, and calculates the user's interest based on the m5 model tree to analyze the analytic events.

关键词： network user behavior data mining user interest m5 model tree

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Regression, Classification and Ensemble machine Learning Approaches to Forecasting Clinical Outcomes in Ischemic Stroke 10th

Regression, Classification and Ensemble Machine Learning App...

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10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Kabir, Ahmedul Ruiz, Carolina Alvarez, Sergio A. moonis, majaz Worcester Polytech Inst 100 Inst Rd Worcester MA 01609 USA Boston Coll Chestnut Hill MA 02467 USA Univ Massachusetts Med Sch Worcester MA 01655 USA

ISBN: (纸本)9783319948065;9783319948058

We applied different machine learning approaches to predict (forecast) the clinical outcome, measured by the modified Rankin Scale (mRS) score, of ischemic stroke patients 90 days after stroke. Regression, multinomial classification, and ordinal regression tasks were considered. m5 model trees followed by bootstrap aggregating as a meta-learning technique produced the best regression results. The same regression technique when used for classification after discretization of the target attribute also performed better than regular multinomial classification. For the ordinal regression task, the logit link function (ordinal logistic regression) outperformed the alternatives. We discuss the methodology used, and compare the results with other standard predictive techniques. We also analyze the results to provide insights into the factors that affect stroke outcomes.

关键词： Ischemic stroke mRS score m5 model tree Bootstrap aggregating Ordinal regression

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Random forest, m5P and regression analysis to estimate the field unsaturated hydraulic conductivity

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APPLIED WATER SCIENCE 2019年第5期9卷 129-129页

作者： Sihag, Parveen Karimi, Sahar mohsenzadeh Angelaki, Anastasia Natl Inst Technol Kurukshetra Dept Civil Engn Kurukshetra Haryana India Univ Tabriz Water Engn Dept Fac Agr Tabriz Iran Univ Thessaly Dept Agr Crop Prod & Rural Environm Volos Greece

Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements of hydraulic conductivity have many difficulties due to spatial variation of the property in the field. Pertaining to this problem, in this study, estimation models have been developed using machine learning methods (m5 tree model and random forest model) in an attempt to estimate the accurate values of unsaturated hydraulic conductivity related to basic soil properties (clay, silt and sand content, bulk density and moisture content). Data set was collected from the experimental measurements of cumulative infiltration using mini disc infiltrometer at the study area (Kurukshetra, India). A multivariate nonlinear regression (mNLR) relationship was derived, and the performance of this model was compared with the machine learning-based models. The evaluation of the results, based on statistical criteria (R-2, RmSE, mAE), suggested that random forest regression model is superior in accurate estimations of the unsaturated hydraulic conductivity of field data relative to m5 model tree and mNLR.

关键词： Unsaturated hydraulic conductivity m5 model tree Random forest multivariate nonlinear regression

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