Prediction of solar radiation in China using different adaptive neuro‐fuzzy methods and <scp>M5</scp> model tree

Wang, Lunche; Kisi, Özgür; Zounemat‐Kermani, Mohammad; Zhu, Zhenfeng; Gong, Wei; Niu, Zigeng; Liu, Hongfu; Liu, Zhengjia

doi:10.1002/joc.4762

Cited by 87 publications

(33 citation statements)

References 78 publications

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“…In addition, Kisi et al [20] developed a dynamic evolving neurofuzzy inference system (DENFIS), which is a fuzzy integrated model, to predict the evaporation of water surface and found that the performance of DENFIS is superior to that of ANFIS as a prediction model for water evaporation. Wang et al [25,26] utilized the soft computing to model the solar radiation in China, and they found that the Multilayer Perceptron (MLP) and radial basis NN models are more accurate in detecting solar radiation at different climatic zones in China; furthermore, the ANFIS and M5 model tree are found to be better in predicting solar radiation at some stations in China. Meanwhile, the multivariate adaptive regression splines (MARS) technique is considered to be an effective tool for prediction and classification of input data for modeling, especially when the input data is limited [27].…”

Section: Introductionmentioning

confidence: 99%

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

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Section: Introductionmentioning

confidence: 99%

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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“…In recent decades, data-driven methods, such as artificial neural networks (ANNs) [8], adaptive neuro fuzzy inference system (ANFIS) [9], support vector machine (SVM) [10], M5-model tree [11], multivariate adaptive regression splines (MARS) [12], and gene expression programing (GEP) [13], have been widely applied for energy demand and solar radiation forecasting studies. For instance, Yadav and Chandel [14] used ANNs to perform an exhaustive review of the prediction of solar radiation.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Rezaie-Balf

Maleki²,

Kim

et al. 2019

Energies

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The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

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“…In recent years, AI techniques have been increasingly used to solve a large number of environmental and water engineering problems. These include evolutionary polynomial regression (EPR) [8,9], ANFIS [10,11], gene expression programming (GEP) [12,13], model tree (MT) [14,15], support vector machine (SVM) [16][17][18], and extreme learning machine (ELM) [8,19]. Various researches have also used AI approaches particularly for river flow forecasting [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Rezaie-Balf

Nowbandegani

Phillips

et al. 2019

Water

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Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

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Prediction of solar radiation in China using different adaptive neuro‐fuzzy methods and M5 model tree

Cited by 87 publications

References 78 publications

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

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