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Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第40期29卷 60792-60810页

作者： Benmouiza, Khalil Amar Telidji Univ Laghouat Semicond & Funct Mat Lab Bp 37 CGhardaia Rd Laghouat 03000 Algeria

The foundation for many solar energy uses as well as economic and environmental concerns is global solar irradiation information. However, due to solar irradiation and measurement variations, reliable worldwide statistics on solar irradiation are frequently impossible or challenging to acquire. In addition, more precise forecasts of solar irradiation play an increasingly important role in electric energy planning and management due to integrating photovoltaic solar systems into power networks. Hence, this paper proposes a new hybrid model for 1-h ahead solar irradiation forecasting called LGC-GMDH (local gravitational clustering-group method of data handling). The novel LGC-GMDH model is based on local clustering that adequately captures the underlying features of the solar irradiation time series. Each cluster is then forecasted using the GMDH method, which is a self-organized system capable of handling very complicated nonlinear problems. Finally, these local forecasts are reconstructed in order to obtain the global forecast. Comparative study between the proposed model and the traditional individual models such as backpropagation neural network (BP), supporting vector machines (SVM), long short-term memory (LTSM), and hybrid models such as BP-MLP, RNN-MLP, LSTM-MLP hybrid wavelet packet decomposition (WPD), convolutional neural network (CNN) with LSTM-MLP, and ANFIS clustering shows that the proposed model overcomes conventional model deficiencies and achieves more precise predicting outcome.

关键词： Solar irradiation forecasting Local gravitational clustering group method of data handling Clustering Phase space reconstruction

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Polynomial neural network-based group method of data handling algorithm coupled with modified particle swarm optimization to predict permeate flux (%) of rectangular sheet-shaped membrane

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CHEMICAL PAPERS 2022年第1期76卷 79-97页

作者： Banik, Anirban Majumder, Mrinmoy Biswal, Sushant Kumar Bandyopadhyay, Tarun Kanti Natl Inst Technol Agartala Dept Civil Engn Jirania 799046 Tripura India Natl Inst Technol Agartala Dept Chem Engn Jirania 799046 Tripura India

The paper discussed a novel approach of polynomial neural network-based group method of data handling coupled with modified particle swarm optimization (PSO) algorithm to predict the permeate flux of rectangular sheet-shaped membrane. In this regard, permeate flux is considered to be a model output, whereas operating pressure, pore size, and feed velocity were considered as model input. It was reported that the polynomial neural network (PNN)-based group method of data handling (GMDH) model has a performance index (PI) value of 0.723 which is higher than in other methods like artificial neural network (ANN), multi-linear regression analysis (MLR), and adaptive neuro-fuzzy inference system (ANFIS). In this study, three typical mathematical test functions were used to evaluate performance of modified PSO. The findings showed that the modified PSO algorithm exhibits better global search ability, computational speed, and stability. Process optimization resulted in optimum operating conditions for operating pressure, pore size, and feed velocity (were 543.69 Pa, 1.40 mu m, and 0.179 m/s), respectively. Sensitivity analysis established pore size to be the most significant parameter.

关键词： Artificial neural network Adaptive neuro-fuzzy inference system group method of data handling Modified particle swarm optimization Membrane Multi-linear regression analysis

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Hybrid particle swarm optimization and group method of data handling for the prediction of ultimate strength of concrete-filled steel tube columns

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ADVANCES IN ENGINEERING SOFTWARE 2024年 195卷

作者： Deng, Chubing Xue, Xinhua Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Architecture & Environm Chengdu 610065 Peoples R China

This study presents a hybrid model coupling particle swarm optimization (PSO) with group method of data handling (GMDH) for predicting the ultimate strength of rectangular concrete-filled steel tube (RCFST) columns. A large database of 490 data samples collected from the existing literature was used to construct the model. Compared with the optimal model among the nine existing models, the coefficient of variation (COV), mean absolute percentage error (MAPE) and root relative squared error (RRSE) values of all datasets of the PSO-GMDH model were decreased by 58.38 %, 69.22 % and 64.27 %, respectively;while the coefficient of determination (R2) 2 ) and a20-index values were increased by 34.32 % and 8.65 %, respectively. The results show that the predicted results of PSO-GMDH model are in good agreement with the experimental results and can accurately predict the ultimate strength of rectangular RCFST columns. In addition, a graphical user interface (GUI) has been developed to facilitate the application of the PSO-GMDH model.

关键词： Rectangular concrete-filled steel tube columns Ultimate strength group method of data handling Particle swarm optimization

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Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

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Chinese Journal of Chemical Engineering 2021年第4期34卷 431-445页

作者： Farzaneh Rezaei Saeed Jafari Abdolhossein Hemmati-Sarapardeh Amir H.Mohammadi Department of Petroleum Engineering Shahid Bahonar University of KermanKermanIran Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Faculty of Environment and Chemical Engineering Duy Tan UniversityDa Nang 550000Vietnam Institut de Recherche en Genie Chimique et Petrolier(IRGCP) Paris CedexFrance Discipline of Chemical Engineering School of EngineeringUniversity of KwaZulu-NatalHoward College CampusKing George V AvenueDurban 4041South Africa

Accurate gas viscosity determination is an important issue in the oil and gas *** approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas *** is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 ***,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the *** absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,*** addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT ***,using leverage technique,valid,suspected and outlier data points were ***,trends of gas viscosity models at different conditions were evaluated.

关键词： Gas Viscosity High pressure high temperature group method of data handling Gene expression programming

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State-of-Health Estimation of Lithium-ion Batteries Based on Singular Value Decomposition and an Improved group method of data handling

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2024年第1期171卷 010525-010525页

作者： Li, Junhong Bai, Guixiang Yan, Jun Hua, Liang Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

Lithium-ion batteries are complex electrochemical systems, and the degradation of their state of health (SOH) is a nonlinear process. Accurate SOH estimation is critical to lithium-ion battery life and safety. This paper uses a data-driven approach to study SOH estimation of lithium-ion batteries. Firstly, this paper uses the singular value decomposition (SVD) method to extract features from the battery charging history data. Secondly, the particle swarm optimization (PSO) algorithm is used to optimize the parameter configuration of the group method of data handling (GMDH). Finally, the SOH estimation is completed using the optimized GMDH. The results show that the proposed PSO-GMDH estimation model maintains an error within 0.89% for estimating its subsequent SOH using historical data of a certain battery, and maintains an error within 0.5% for estimating the SOH of another battery of the same model using historical data of multiple batteries. At the same time, the results also show that the PSO-GMDH estimation model has higher estimation accuracy than the GMDH model without parameter optimization.

关键词： Lithium-ion battery SOH estimation Singular value decomposition Particle swarm optimization group method of data handling

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A novel hybrid model based on grey wolf optimizer and group method of data handling for the prediction of monthly mean significant wave heights

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OCEAN ENGINEERING 2023年第1期284卷

作者： Xie, Jingxuan Xue, Xinhua Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

Significant wave height prediction is challenging owing to the nonlinear and nonsmooth attributes of wave heights. This study presents a hybrid model coupling group method of data handling (GMDH) with grey wolf optimizer (GWO) for the prediction of significant wave heights. The datasets were assembled from three different observations, Stations 41001, 41002 and 44004 in the Atlantic;the datasets of Stations 41001 and 41002 were used for training, and those of Station 44004 were used for testing. The performance of the GWO-GMDH model was compared with four artificial intelligence models, the GMDH, gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and back propagation (BP) neural network models, and one empirical equation derived by Buckingham & pi;-theorem. Both regression plots and statistical indices (e.g., correlation coefficient (R), root mean squared error (RMSE), mean squared error (MSE) and mean absolute percentage error (MAPE)) were adopted to evaluate the performance of the hybrid GWO-GMDH model. The MSE, RMSE, MAPE and R values were 0.041, 0.202, 7.353% and 0.953, respectively, for the training datasets and 0.031, 0.175, 7.598% and 0.941, respectively, for the testing datasets. Compared with the single GMDH model, the statistical indices of the training datasets of the hybrid GWO-GMDH model were almost the same;however, the MSE, RMSE and MAE values decreased by 24.39%, 13.37% and 7.95%, respectively, and the R value increased by 2.28% in the testing datasets. Compared with the GEP, BP, and ANFIS models and empirical equation models, the GWOGMDH model also shows high accuracy and robustness, especially compared with empirical formulations. In addition, a graphical user interface (GUI) was developed to facilitate the application of practical engineering use.

关键词： Significant wave height group method of data handling Grey wolf optimizer Machine learning

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ML-based group method of data handling: an improvement on the conventional GMDH

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COMPLEX & INTELLIGENT SYSTEMS 2021年第6期7卷 2949-2960页

作者： Amiri, Mehdi Soleimani, Seyfollah Arak Univ Fac Engn Dept Comp Engn Arak *** Iran

Machine learning (ML) has been recognized as a feasible and reliable technique for the modeling of multi-parametric datasets. In real applications, there are different relationships with various complexities between sets of inputs and their corresponding outputs. As a result, various models have been developed with different levels of complexity in the input-output relationships. The group method of data handling (GMDH) employs a family of inductive algorithms for computer-based mathematical modeling grounded on a combination of quadratic and higher neurons in a certain number of variable layers. In this method, a vector of input features is mapped to the expected response by creating a multistage nonlinear pattern. Usually, each neuron of the GMDH is considered a quadratic partial function. In this paper, the basic structure of the GMDH technique is adapted by changing the partial functions to enhance the complexity modeling ability. To accomplish this, popular ML models that have shown reasonable function approximation performance, such as support vector regression and random forest, are used, and the basic polynomial functions in the GMDH are replaced by these ML models. The regression feasibility and validity of the ML-based GMDH models are confirmed by computer simulation.

关键词： group method of data handling Machine learning ML-based group method of data handling

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Enhancing predictive ability of optimized group method of data handling (GMDH) method for wildfire susceptibility mapping

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AGRICULTURAL AND FOREST METEOROLOGY 2023年第1期339卷

作者： Tran, Trang Thi Kieu Bateni, Sayed M. Rezaie, Fatemeh Panahi, Mahdi Jun, Changhyun Trauernicht, Clay Neale, Christopher M. U. Univ Hawaii Manoa Dept Civil & Environm Engn Water Resources Res Ctr Honolulu HI 96822 USA Korea Inst Geosci & Mineral Resources KIGAM Geosci Data Ctr 124 Gwahak Ro Daejeon 34132 South Korea Korea Univ Sci & Technol Dept Geophys Explorat 217 Gajeong Ro Daejeon 34113 South Korea Chosun Univ Dept Comp Engn Gwangju 61452 South Korea Chung Ang Univ Coll Engn Dept Civil & Environm Engn Seoul 06974 South Korea Univ Hawaii Manoa Dept Nat Resources & Environm Management Honolulu HI 96822 USA Univ Nebraska Daugherty Water Food Global Inst Lincoln NE USA

Wildfire is one of the most significant environmental challenges and causing damage to ecosystems, habitats, infrastructure and especially human lives. Thus, spatial assessment of fire risk is vital to reduce the impacts of wildfires. This paper introduces an effective wildfire susceptibility mapping framework by applying a group method of data handling (GMDH) with three metaheuristic methods of biogeography-based optimization (BBO), imperialist competitive algorithm (ICA), and teacher-learning-based optimization (TLBO). A total of 13 wildfire influencing factors, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index, valley depth, wind speed, normalized difference vegetation index, rainfall, temperature, distance to roads, and distance to rivers were used in the proposed models for wildfire susceptibility mapping of Oahu Island in Hawaii, USA. The predictive performance of models was evaluated by the root mean square error (RMSE), mean squared error (MSE), and area under the receiver operating characteristic curve (AUC). The findings demon-strated that the three hybrid models outperformed the standalone GMHD in the study area. For example, in the validation phase, the models of GMDH-TLBO, GMDH-ICA, GMDH-BBO, and GMDH produced AUC values of 0.81, 0.80, 0.79, and 0.75, respectively. The MSE values for the GMDH, GMDH-ICA, GMDH-BBO, and GMDH-TLBO models were 0.048, 0.026, 0.030, and 0.020 and the RMSE values were 0.2119, 0.162, 0.17, and 0.142, respectively. Thus, the GMDH-TLBO and GMDH were the most and least effective algorithms because they had the highest and lowest predictive accuracy. Moreover, distance to road is the most effective factor for detecting wildfire-prone areas. It is followed by temperature, slope, and elevation. By localizing the input data, the methodology can be applied to other regions for wildfire susceptibility mapping, facilitating wildfire mitigation and prevention.

关键词： Wildfire susceptibility mapping group method of data handling Optimization Geo-environmental factors Hawaii

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A new correlation for accurate prediction of oil formation volume factor at the bubble point pressure using group method of data handling approach

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartB期208卷 109410-109410页

作者： Ayoub, Mohammed Abdalla Elhadi, A. Fatherlhman, Diab Saleh, M. O. Alakbari, Fahd Saeed Mohyaldinn, Mysara Eissa Univ Teknol PETRONAS Petr Engn Dept Seri Iskandar 32610 Perak Malaysia

Pressure-Volume-Temperature (PVT) crude oil properties play a significant role in reservoir evaluation and field planning. PVT properties are usually determined through laboratory experiments on representative fluid samples. However, during the preliminary stages of exploration and appraisal, such data might not be available;hence, it is frequent to use empirical correlations to predict PVT properties. Oil formation volume factor (FVF) is one of the PVT properties used to convert the measured oil flow rate from surface conditions to reservoir conditions. In this paper, the group method of data handling (GMDH) has been used to predict the oil FVF at the bubble point pressure as a function of gas solubility, reservoir temperature, oil API gravity, and gas specific gravity. A total of 625 data sets were collected from published literature. Then, the data were divided into four sets: training, validation, testing, and deployment, with the ratio of 2:1:1:1. The results of the proposed correlation are compared against seven other correlations used in the petroleum industry. Also, trend analysis has been performed to confirm that the proposed correlation is physically sound. From the results, the proposed correlation is proven to accurately predict the oil FVF at the bubble point pressure with an average absolute percentage error AAPE of 1.333% and correlation coefficient of 0.995 for the deployment set.

关键词： group method of data handling Oil formation volume factor PVT properties The curse of dimensionality Statistical analyses Graphical analyses

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Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

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ENERGY 2022年第PartA期239卷 121915-121915页

作者： Mulashani, Alvin K. Shen, Chuanbo Nkurlu, Baraka M. Mkono, Christopher N. Kawamala, Martin China Univ Geosci Key Lab Tecton & Petr Resources Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Sch Earth Resources Dept Petr Geol Wuhan 430074 Peoples R China Mbeya Univ Sci & Technol Coll Engn & Technol Dept Geosci & Min Technol Box 131 Mbeya Tanzania

Permeability is the key variable for reservoir characterization used for estimating the flow patterns and volume of hydrocarbons. Modern computer advancement has highlighted the use of machine learning approaches such as group method of data handling (GMDH) in predicting permeability. However, the widely employed GMDH has intrinsic problems in its application. Therefore, the objective of this study is to present an enhanced GMDH based modified Levenberg-Marquardt (LM) as an improved alternative to conventional GMDH in predicting permeability from well logs. The study used natural gamma-ray, standard resolution formation density, limited effective porosity, shale volume of rock, and thermal neutron porosity well logs as input variables. Results show that an enhanced method has a reasonable reduction in processing time with high accuracy. Compared to conventional GMDH and backpropagation neural networks (BPNN), the GMDH-LM used 30% less computation time and performed excellently during training with the least error values of 0.092 and 0.018 for RMSE and MAE. Likewise, good results were observed during testing, obtaining the least error values of 0.679 and 0.056 for RMSE and MAE respectively. The modified generalization performance of GMDH-LM makes it an improved form of GMDH and can be adopted as an improved alternative in predicting permeability. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network permeability Well logs group method of data handling

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