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Data mining techniques for thermophysical properties of refrigerants

ENERGY CONVERSION AND mANAGEmENT 2009年第2期50卷 399-412页

作者： Kuecueksille, Ecir Ugur Selbas, Resat Sencan, Arzu Suleyman Demirel Univ Dept Mech Educ Tech Educ Fac TR-32260 Isparta Turkey

This study presents ten modeling techniques within data mining process for the prediction of thermophysical properties of refrigerants (R134a, R404a, R407c and R410a). These are linear regression (LR), multi layer perception (mLP), pace regression (PR), simple linear regression (SLR), sequential minimal optimization (SmO), I(Star, additive regression (AR), m5 model tree, decision table (DT), m5'Rules models. Relations depending on temperature and pressure were carried out for the determination of thermophysical properties as the specific heat capacity, viscosity, heat conduction coefficient, density of the refrigerants. Obtained model results for every refrigerant were compared and the best model was investigated. Results indicate that use of derived formulations from these techniques will facilitate design and optimize of heat exchangers which is component of especially vapor compression refrigeration system. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Refrigerant Thermophysical properties Data mining Linear regression multi layer perception Pace regression Simple linear regression Sequential minimal optimization KStar Additive regression m5 model tree Decision table m5 ' Rules

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Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods

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NEURAL COmPUTING & APPLICATIONS 2017年第12期28卷 3993-4010页

作者： Zhuang, Zhaoyi Ben, Xianye Yan, Rui Pang, Jianhua Li, Yongbin Shandong Jianzhu Univ Coll Thermal Energy Engn Jinan 250101 Shandong Peoples R China Shandong Univ Sch Informat Sci & Engn 27 Shanda South Rd Jinan 250100 Shandong Peoples R China Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA

Nowadays, the ground-coupled heat pump (GCHP) systems have been recognized as one of the most energy-efficient systems for heating, cooling and hot water supply in both residential and commercial buildings. However, the heat transfer of ground heat exchanger (GHE) involves in large spatial scales, long time span and complex influential factors. We develop a data mining framework constructed by using 1998 experimental data to study the effects of 12 input variables composed of seven borehole parameters, two U-tube parameters, two ground parameters and one circulating liquid parameter to accurately predict the heat transfer performance of GHE for GCHP systems in 10 years. Hence, selecting a suitable input configuration to improve the energy efficiency has important sustainability benefits. The role of each of independent variable explaining the output variables is analyzed by partial least squares regression. Furthermore, support vector regression and m5 model tree are, respectively, used to predict the heat transfer performance. Extensive simulations show that we can predict the average quantity of heat exchanger, temperature of ground around GHE, inlet temperature of heat pump unit with very low level of error.

关键词： Ground-coupled heat pump (GCHP) Ground heat exchanger (GHE) Partial least squares regression (PLSR) Support vector regression (SVR) m5 model tree

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Water eutrophication assessment relied on various machine learning techniques: A case study in the Englishmen Lake (Northern Spain)

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ECOLOGICAL modelLING 2019年 404卷 91-102页

作者： Garcia Nieto, P. J. Garcia-Gonzalo, E. Alonso Fernandez, J. R. Diaz muinz, C. Oviedo Univ Math Dept Fac Ciencias Oviedo 33007 Spain Minist Agr Fishing Food & Environm Cantabrian Basin Author Oviedo 33071 Spain

Algal atypical productivity, also called eutrophication, is a process where the phosphorus content in the water, together with aquatic flora, increases, causing high Chlorophyll levels and affecting the water quality and its possible applications. Therefore, it is important to be able to anticipate such circumstance to avoid subsequent hazards. In this paper, a model that estimates the conditions where an abnormal growth of algae in reservoirs and lakes takes place is built. This method combines artificial bee colony and support vector machines algorithms to predict the eutrophication taking into account physical-chemical and biological data sampled in the Englishmen Lake and posterior analysis in a laboratory. The support vector machines parameters are tuned by means of the artificial bee colony algorithm, improving the accuracy of the procedure. For comparison sake, two other methods have been used to construct additional models, the m5 model tree and multilayer perceptron network. Two objectives are covered by this study: the forecasting of the algal proliferation by means of the model and, the ranking of the relative importance of the independent variables. Indeed, coefficients of determination of 0.92 for the Chlorophyll and 0.90 for the Total phosphorus concentrations were obtained using this hybrid method that optimizes the regression parameters. Furthermore, the results obtained with m5 model tree and multilayer perceptron network techniques were clearly worse. Finally, conclusions of this work are drawn in the final section.

关键词： Support vector machine (SVm) Artificial bee colony (ABC) Artificial neural networks (ANNs) m5 model tree Algal atypical productivity in lakes Regression analysis

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The improvement of wavelet-based multilinear regression for suspended sediment load modeling by considering the physiographic characteristics of the watershed

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WATER SCIENCE AND TECHNOLOGY 2023年第7期87卷 1791-1802页

作者： Nejatian, Niloofar Yavary Nia, mohsen Yousefyani, Hooshyar Shacheri, Fatemeh Yavari Nia, melika CUNY City Coll Dept Civil Engn New York NY 10031 USA Univ Florida Dept Civil & Coastal Engn Gainesville FL USA Cent Michigan Univ Dept Engn & Technol Mt Pleasant MI USA Virginia Tech Univ Dept Biol Syst Engn Blacksburg VA USA Politecn Milan Dept Civil & Environm Engn Milan Italy

The aim of this study is to model a relationship between the amount of the suspended sediment load by considering the physiographic characteristics of the Lake Urmia watershed. For this purpose, the information from different stations was used to develop the sediment estimation models. Ten physiographic characteristics were used as input parameters in the simulation process. The m5 model tree was used to select the most important features. The results showed that the four factors of annual discharge, average annual rainfall, form factor and the average elevation of the watershed were the most important parameters, and the multilinear regression models were created based on these factors. Furthermore, it was concluded that the annual discharge was the most influential parameter. Then, the stations were divided into two homogeneous classes based on the selected features. To improve the efficiency of the m5 model, the non-stationary rainfall and runoff signals were decomposed into sub-signals by the wavelet transform (WT). By this technique, the available trends of the main raw signals were eliminated. Finally, the models were developed by multilinear regressions. The model using all four factors had the best performance (DC = 0.93, RmSE = 0.03, mE = 0.05 and RE = 0.15).

关键词： feature selection m5 model tree physiographic characteristics suspended sediment load wavelet transform

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Data mining based on wavelet and decision tree for rainfall-runoff simulation

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HYDROLOGY RESEARCH 2019年第1期50卷 75-84页

作者： Nourani, Vahid Tajbakhsh, Ali Davanlou molajou, Amir Univ Tabriz Fac Civil Engn Dept Water Resources Engn POB 51666 Tabriz Iran Near East Univ Dept Civil Engn POB 99138Mersin 10 Nicosia North Cyprus Turkey Iran Univ Sci & Technol Fac Civil Engn Dept Water Resources Engn Tehran Iran

This study introduced a new hybrid model (Wavelet-m5 model) which combines the wavelet transforms and m5 model tree for rainfall-runoff modeling. For this purpose, the main time series were decomposed to several sub-signals by the wavelet transform, at first. Then, the obtained sub-time series were imposed as input data to m5 model tree, and finally, the related linear regressions were presented by m5 model tree. This new technique was applied on the monthly time series of Sardrud catchment and the results were also compared with other models like WANN and sole m5 model tree. The results showed that the accuracy of the proposed model is better than the previous models and also indicated the effect of data pre-processing on the performance of m5 model tree. The determination coefficient of the training stage was 0.80 and improved 31% than the m5 model tree for Sardrud catchment which is recognized as a normal watershed with a regular four seasons' pattern.

关键词： decision tree m5 model tree multi-linear model rainfall-runoff modeling wavelet transform

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Prediction of diffuse photosynthetically active radiation using different soft computing techniques

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QUARTERLY JOURNAL OF THE ROYAL mETEOROLOGICAL SOCIETY 2017年第706期143卷 2235-2244页

作者： Wang, Lunche Hu, Bo Kisi, Ozgur Zounemat-Kermani, mohammad Gong, Wei China Univ Geosci Sch Earth Sci Lab Crit Zone Evolut Lumo Rd 388 Wuhan 430074 Hubei Peoples R China Chinese Acad Sci Inst Atmospher Phys State Key Lab Atmospher Boundary Phys & Atmospher Beijing Peoples R China Ilia State Univ Sch Nat Sci & Engn Tbilisi Georgia Shahid Bahonar Univ Kerman Dept Water Engn Kerman Iran Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Hubei Peoples R China Collaborat Innovat Ctr Geospatial Technol Wuhan Hubei Peoples R China

Knowledge of diffuse photosynthetically active radiation (PAR(d)) is important for many applications dealing with climate change, environmental engineering and terrestrial productivity. It is necessary to estimate the PAR(d) using different techniques due to the absence of direct observations of this radiometric flux inmost parts of the world. In this study, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC) and m5 model tree (m5tree) are optimized and applied for predicting hourly PAR(d) in different ecosystems. Hourly climatic variables at six stations from the AmeriFlux network are used for training, validating and testing the above models. It is observed that the mean bias error (mBE) values range -11 to +18%, -16 to +9% and -10 to +9% for ANFIS-GP, ANFIS-SC and m5treemodels, respectively;root mean square errors (RmSE) range 25-44%, 22-41% and 29-51%, respectively for different stations in the testing period. The results show that the ANFIS-SC model can generally bring more accurate estimations than other models, and the statistical indices (mBE and RmSE) at CA_Gro stations (mixed forests) are lower than other stations for each model. The models underestimate some high PAR(d) values (>900 mu mol m(-2) s(-1)) for some stations, which may due to the differences of data ranges and distributions. This study will lay the foundation for accurately mapping regional and global distributions of PAR(d) and its associated ecological applications.

关键词： diffuse photosynthetically active radiation ANFIS-GP ANFIS-SC m5 model tree model comparison

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Subset modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

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WATER RESOURCES mANAGEmENT 2018年第3期32卷 1101-1116页

作者： Keshtegar, Behrooz Kisi, Ozgur Arab, Hamed Ghohani Zounemat-Kermani, mohammad Univ Zabol Dept Civil Engn PB 9861335-856 Zabol Iran Ilia State Univ Fac Nat Sci & Engn Tbilisi 0162 Georgia Univ Sistan & Baluchestan Dept Civil Engn Zahedan Iran Shahid Bahonar Univ Kerman Water Engn Dept Kerman Iran

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET0). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET0 was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the m5 model tree (m5tree), ANFIS models and ANN. mean absolute error (mAE), root mean square error (RmSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of m5tree (from 15.3% to 32.5% in RmSE, from 14.4% to 24.2% in mAE), ANN (from 24.3% to 65.3% in RmSE, from 34.1% to 47% in mAE) and ANFIS (from 17.4% to 35.4% in RmSE, from 10.8% to 28.3% in mAE) models was significantly increased using subset ANFIS for estimating da ily ET0..

关键词： Reference evapotranspiration ANFIS m5 model tree ANN Subset ANFIS

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Derivation of New Equations for Prediction of Principal Ground-motion Parameters using m5 Algorithm

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JOURNAL OF EARTHQUAKE ENGINEERING 2016年第6期20卷 910-930页

作者： Kaveh, A. Bakhshpoori, T. Hamze-Ziabari, S. m. Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran 16 Iran

In this study, m5 model tree is used to develop a model for prediction of peak time-domain strong ground motion parameters. The main advantages of model trees are that can be easily developed and their formulas are simple and understandable. Selected data from Pacific Earthquake Engineering Research Center (PEER) are used to train the proposed model. Earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms are used as input parameters. The developed m5 based formulas are compared with those of well-known empirical and soft computing based models. The accuracy of the model is evaluated by statistical error parameters.

关键词： Prediction of Strong Ground motion Parameters Earthquake Hazard Peak Time-Domain m5 model tree PEER

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Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle

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ENVIRONmENTAL RESEARCH 2017年 155卷 141-166页

作者： Deo, Ravinesh C. Downs, Nathan Parisi, Alfio V. Adamowski, Jan F. Quilty, John m. Univ Southern Queensland Inst Agr & Environm IAg & E ICACS Sch Agr Computat & Environm Sci Springfield Cent Qld 4300 Australia McGill Univ Fac Agr & Environm Sci Dept Bioresource Engn Ste Anne De Bellevue PQ H9X 3V9 Canada

Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (theta(s)) data, was developed using an extreme learning machine (ELm) algorithm. An ELm algorithm typically serves to address complex and ill-defined forecasting problems. UV spectroradiometer situated in Toowoomba, Australia measured daily cycles (05001700 h) of UVI over the austral summer period. After trialling activations functions based on sine, hard limit, logarithmic and tangent sigmoid and triangular and radial basis networks for best results, an optimal ELm architecture utilising logarithmic sigmoid equation in hidden layer, with lagged combinations of 0, as the predictor data was developed. ELm's performance was evaluated using statistical metrics: correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe efficiency coefficient (E-NS), root mean square error (RAISE), and mean absolute error (mAE) between observed and forecasted UVI. Using these metrics, the ELm model's performance was compared to that of existing methods: multivariate adaptive regression spline (mARS), m5 model tree, and a semi-empirical (Pro6UV) clear sky model. Based on RmSE and mAE values, the ELm model (0.255, 0.346, respectively) outperformed the mARS (0.310, 0.438) and m5 model tree (0.346, 0.466) models. Concurring with these metrics, the Willmott's Index for the ELm, mARS and m5 model tree models were 0.966, 0.942 and 0.934, respectively. About 57% of the ELm model's absolu

关键词： Extreme learning machine Solar ultraviolet index (UVI) multivariate adaptive regression splines m5 model tree Real-time solar forecasting

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modeling of thermodynamic properties of refrigerant/absorbent couples using data mining process

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ENERGY CONVERSION AND mANAGEmENT 2007年第2期48卷 470-480页

作者： Sencan, Arzu Suleyman Demirel Univ Dept Mech Educ Tech Educ Fac TR-32260 Isparta Turkey

In this paper, in order to determine thermodynamic properties of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl), a data mining process was used. These fluid couples can be used in absorption heat pump systems, and their main advantage is that they do not cause ozone depletion. In order to train the network, limited experimental measurements were used as training and test data. In the present study, linear regression (LR), pace regression (PR), sequential minimal optimization (SmO), m5 model tree, m5'Rules and back propagation neural network (BPNN) models are applied within the data mining process for determining the specific volume of the methanol/LiBr and methanol/LiCl fluid couples. The best result was obtained by using the back propagation model. A new formulation is presented for determination of the specific volumes of the two refrigerant/absorbent couples. The use of this new formulation, which can be employed with any programming language or spreadsheet program for estimation of the specific volumes of fluid couples, as described in this paper, may make the use of dedicated BPNN software unnecessary. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： methanol/LiBr methanol/LiCl thermodynamic properties pace regression SmO m5 model tree m5'Rules back propagation neural network

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