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modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and m5 model tree

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JOURNAL OF HYDROLOGY 2018年 559卷 499-509页

作者： Heddam, Salim Kisi, Ozgur Univ 20 Aout 1955 SKIKDA Agron Dept Fac Sci Hydraul Div Route EL HADAIKBP 26 Skikda Algeria Ilia State Univ Fac Nat Sci & Engn Tbilisi 0162 Georgia

In the present study, three types of artificial intelligence techniques, least square support vector machine (LSSVm), multivariate adaptive regression splines (mARS) and m5 model tree (m5T) are applied for modeling daily dissolved oxygen (DO) concentration using several water quality variables as inputs. The DO concentration and water quality variables data from three stations operated by the United States Geological Survey (USGS) were used for developing the three models. The water quality data selected consisted of daily measured of water temperature (TE, degrees C), pH (std. unit), specific conductance (SC, mu S/cm) and discharge (DI cfs), are used as inputs to the LSSVm, mARS and m5T models. The three models were applied for each station separately and compared to each other. According to the results obtained, it was found that: (i) the DO concentration could be successfully estimated using the three models and (ii) the best model among all others differs from one station to another. (C) 2018 Elsevier B.V. All rights reserved.

关键词： River water quality modelling Dissolved oxygen Least square support vector machine multivariate adaptive regression splines m5 model tree

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Daily mean air temperature estimation from mODIS land surface temperature products based on m5 model tree

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INTERNATIONAL JOURNAL OF CLImATOLOGY 2013年第15期33卷 3174-3181页

作者： Emamifar, Saeed Rahimikhoob, Ali Noroozi, Ali Akbar Univ Tehran Dept Irrigat & Drainage Engn Tehran Iran Soil Conservat & Watershed Management Res Ctr Tehran Iran

The use of satellite data to estimate air temperature (T-a) near the Earth's surface has become an effective way for a large area with high spatial and temporal resolution. In this study, m5 model tree was used to estimate T-a in Khuzestan province (in the southwest of Iran) using satellite remotely sensed land surface temperature (T-s) data acquired by the mODerate Resolution Imaging Spectroradiometer (mODIS)-Terra sensor. The input variables for the m5 model tree were the daytime and nighttime mODISTs, extraterrestrial solar radiation and Julian day. A total of 365 images of mOD11A1T(s) product for the year 2007, covering the area of this study were collected from the Land Processes Distributed Active Archive Center (LP DAAC). The results of this study showed that mean daily T-a can be estimated with acceptable levels of the statistical indicators from mODIS data and from the two geographic parameters using the m5 model. Root mean square error and R-2 for the comparison between observed and estimated T-a for the tested data are 2.3 and 0.96 degrees C, respectively.

关键词： air temperature m5 model tree mODIS-Terra sensor Iran

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Predicting the critical superconducting temperature using the random forest, mLP neural network, m5 model tree and multivariate linear regression

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ALEXANDRIA ENGINEERING JOURNAL 2024年 86卷 144-156页

作者： Nieto, Paulino Jose Garcia Gonzalo, Esperanza Garcia Garcia, Luis Alfonso menendez Alvarez-de Prado, Laura Sanchez, Antonio Bernardo Univ Oviedo Fac Sci Dept Math Oviedo 33007 Spain Univ Leon Higher & Tech Sch Min Engn Dept Min Technol Topog & Struct Campus Vegazana S-N Leon 24071 Spain

Using a random forest regression (RFR) machine learning technique, the critical temperature (Tc) of a superconductor was predicted in the context of Industry 4.0 in this study using features derived from the material's physico-chemical properties, containing atomic mass, electron affinity, atomic radius, valence, and thermal conductivity. The same experimental data were also fitted with multilayer perceptron (mLP) artificial neural networks (ANN), m5 model tree and multivariate linear regression (mLR) model for comparison. The current investigation's findings show that the proposed RFR-relied model can successfully forecast the critical temperature of a superconductor. Additionally, the Tc estimate was reached with a correlation coefficient of 0.9565 and a coefficient of determination 0.9146, when the observed dataset was used to test this unique technique. Additionally, the outcomes from the mLP, m5, and mLR models are obviously worse than those from the RFR-relied model. When it comes to fully comprehending the superconductivity, this investigation is noteworthy. Regarding forecasting effectiveness and feature reduction rate, the RFR approach has obvious advantages and generalizability, and it also demonstrates suitability for high-temperature superconductor Tc forecasting. In fact, it offers a practical and affordable approach to data-driven superconductor investigation.

关键词： Critical superconducting temperature Random forest regression (RFR) technique Artificial neural networks (ANNs) m5 model tree multivariate linear regression (mLR)

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Comparison between m5 model tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment

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WATER RESOURCES mANAGEmENT 2014年第3期28卷 657-669页

作者： Rahimikhoob, Ali Univ Tehran Dept Irrigat & Drainage Engn Tehran Iran

This paper describes a detailed evaluation of the performance and characteristic behaviour of feed-forward artificial neural network (ANN) and m5 model tree for estimating reference evapotranspiration (ET0) at four meteorological sites in an arid climate. The input variables for these models were the maximum and minimum air temperature, air humidity and extraterrestrial radiation. The FAO-56 Penman-monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that the ANN estimated ET0 better than the m5 model tree but both models performed well for the study area and yielded results close to the FAO56-Pm method. Root mean square error and R-2 for the comparison between reference and estimated ET0 for the tested data using the proposed ANN model are 5.6 % and 0.98, respectively. For the m5 model tree method these values are 8.9 % and 0.98, respectively. The overall results are of significant practical use because the temperature and Humidity-based model can be used when radiation and wind speed data are not available.

关键词： m5 model tree Neural networks Reference evapotranspiration Arid environment

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Air Quality modeling Using the PSO-SVm-Based Approach, mLP Neural Network, and m5 model tree in the metropolitan Area of Oviedo (Northern Spain)

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ENVIRONmENTAL modelING & ASSESSmENT 2018年第3期23卷 229-247页

作者： Garcia Nieto, P. J. Garcia-Gonzalo, E. Bernardo Sanchez, A. Rodriguez miranda, A. A. Univ Oviedo Fac Sci Dept Math Oviedo 33007 Spain Univ Leon Dept Min Technol Topog & Struct E-24071 Leon Spain

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (mLP) and the m5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the mLP and m5 model tree prediction methods in terms of statistical estimators and testing performances.

关键词： Support vector machines (SVms) Particle swarm optimization (PSO) Artificial neural networks (ANNs) m5 model tree Air quality control and modeling

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Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSm, mARS and m5 model tree

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2018年第Part1期81卷 330-341页

作者： Keshtegar, Behrooz mert, Cihan Kisi, Ozgur Univ Zabol Dept Civil Engn PB *** Zabol Iran Int Black Sea Univ Fac Comp Technol & Engn Tbilisi Georgia Ilia State Univ Sch Nat Sci & Engn Tbilisi Georgia

In this study, four different heuristic regression methods including Kriging, response surface method (RSm), multivariate adaptive regression (mARS) and m5 model tree (m5tree) have been investigated for accurate estimating of solar radiation with different input data. monthly solar radiation (SR) from Adana and Antakya stations, which are located in Eastern mediterranean Region of Turkey is estimated based on the input data of maximum temperature (T-max), minimum temperature (T-min), sunshine hours (H-s), wind speed (W-s), and relative humidity (RH). In Adana station, the best mARS model provided slightly better accuracy than the Kriging, RSm and m5tree while the Kriging was found to be the better than the mARS, RSm and m5tree in Antakya station. The predictions of m5tree model are shown inaccurate results for both maximum errors and minimum agreement compared to another models. The effect of periodicity input is examined to obtain the accurate predictions of solar radiation for these stations based on the four heuristic -based modeling Kriging, mARS, RSm, m5tree approaches. Periodicity input data improved the root mean square errors of the best mARS, RSm, m5tree and Kriging models as 34%, 37%, 46% and 39% for Adana station and by 51%, 47%, 38% and 49% for Antakya station, respectively. The periodic Kriging models performed superior to the periodic mARS, RSm and m5tree models.

关键词： Solar radiation Kriging RSm mARS m5 model tree modeling

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Groundwater level simulation using gene expression programming and m5 model tree combined with wavelet transform

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2020年第8期65卷 1430-1442页

作者： Bahmani, Ramin Solgi, Abazar Ouarda, Taha B. m. J. INRS ETE Canada Res Chair Stat Hydroclimatol Quebec City PQ Canada Shahid Chamran Univ Ahvaz Dept Water Resources Engn Fac Water Sci Engn Ahwaz Iran

In order to understand and adequately manage hydrological stress, it is necessary to simulate groundwater levels accurately. In this research, gene expression programming (GEP) and m5 model tree (m5) are used to simulate monthly groundwater levels. The models are combined with wavelet transform to produce two hybrid models: wavelet gene expression programming (WGEP) and wavelet m5 model tree (Wm5). For the simulation, groundwater level, temperature and precipitation values from three observation wells and one meteorological station, located in Iran, are used. The results indicate that the hybrid models, WGEP and Wm5, lead to a better performance than the simple models, GEP and m5. The performance of the two hybrid models is similar. It is also observed that selecting a suitable time lag for inputs plays an important role in the accuracy of the simple models. The selection of a suitable decomposition level strongly affects the accuracy of hybrid models.

关键词： groundwater level gene expression programming hybrid model m5 model tree wavelet transform

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Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and m5 model tree techniques

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mATERIALS AND STRUCTURES 2016年第10期49卷 4319-4334页

作者： mansouri, Iman Ozbakkaloglu, Togay Kisi, Ozgur Xie, Tianyu Birjand Univ Technol Dept Civil Engn POB 97175-569 Birjand Iran Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia Canik Basari Univ Dept Civil Engn Samsun Turkey

This paper studies the ability of artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), multivariate adaptive regression splines (mARS) and m5 model tree (m5tree) techniques to predict ultimate conditions of fiber-reinforced polymer (FRP)-confined concrete. A large experimental test database that consists of over 1000 axial compression tests results of FRP-confined concrete specimens assembled from the published literature was used to train, test, and validate the models. The modeling results show that the ANN, ANFIS, mARS and m5tree models fit well with the experimental test data. The m5tree model performs better than the remaining models in predicting the hoop strain reduction factor and strength enhancement ratio, whereas the ANN model provided the most accurate estimates of the strain enhancement ratio. Performances of the proposed models are also compared with those of the existing conventional and evolutionary algorithm models, which indicate that the proposed ANN, ANFIS, mARS and m5tree models exhibit improved accuracy over the existing models. The predictions of each proposed model are subsequently used to establish the interdependence of critical parameters and their influence on the behavior of FRP-confined concrete, which are discussed in the paper.

关键词： Neuro fuzzy Neural network multivariate adaptive regression splines m5 model tree Fiber-reinforced polymer (FRP) Confined concrete

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modeling algal atypical proliferation using the hybrid DE-mARS-based approach and m5 model tree in La Barca reservoir: A case study in northern Spain

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ECOLOGICAL ENGINEERING 2019年 130卷 198-212页

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

Algal atypical proliferation is a consequence of water fertilization (also called eutrophication) and one of the main causes of the degradation of reservoir and lake ecosystems. Its intensification during the last decades has led the stakeholders to seek water management and restoration solutions, including those based on modelling approaches. In this way, this paper presents one reservoir eutrophication modelling based on a new hybrid algorithm that combines multivariate adaptive regression splines (mARS) and differential evolution (DE) to estimate the algal abnormal proliferation from physical-chemical and biological variables. This technique involves the optimization of the mARS hyperparameters during the training process. Additionally, an m5 model tree was fitted to the experimental data for comparison purposes. Apart from successfully forecasting algal atypical growth (coefficients of determination equal to 0.83 and 0.91), the model showed here can establish the significance of each biological and physical-chemical parameter of the algal enhanced growth. Finally, the main conclusions of this research work are exposed.

关键词： multivariate adaptive regression splines (mARS) Differential evolution (DE) m5 model tree Algal abnormal productivity in reservoirs Regression analysis

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modeling of Sediment Yield Prediction Using m5 model tree Algorithm and Wavelet Regression

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WATER RESOURCES mANAGEmENT 2014年第7期28卷 1991-2003页

作者： Goyal, manish Kumar Indian Inst Technol Dept Civil Engn Gauhati India

The forecast of the sediment yield generated within a watershed is an important input in the water resources planning and management. The methods for the estimation of sediment yield based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment yield. The focus of present study is to deal with the development of decision tree based m5 model tree and wavelet regression models for modeling sediment yield in Nagwa watershed in India. A comparison is also performed with the artificial neural network (ANN) model for streamflow forecasting. The root mean square errors (RmSE), Nash-Sutcliff efficiency index (N-S Index), and correlation coefficient (R) statistics are used for the statistical criteria. A comparative evaluation of the performance of m5 model tree and wavelet regression versus ANN clearly shows that m5 model tree and wavelet regression can prove more useful than ANN models in estimation of sediment yield. Further, m5 model tree offers explicit expressions for use by design engineers.

关键词： Streamflow m5 model tree Discrete wavelet transform Regression Forecast

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