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New input selection procedure for machine learning methods in estimating daily global solar radiation

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Abstract

Selection of optimal model inputs is a challenging issue particularly for non-linear and dynamic systems. In this study, a new input selection method, procrustes analysis (PA), was implemented and compared with gamma test (GT) for estimating daily global solar radiation (Rs). The PA and GT were applied for modeling with the non-linear models of artificial neural networks (ANNs) and support vector machines (SVMs). Goodness-of-fit of the models was evaluated by the coefficient of correlation (CC), root-mean-square error (RMSE), and Nash-Sutcliffe model efficiency coefficient (NS). The uncertainty of the model outputs was determined using 95PPU% (p-factor) and d-factor. In this study, we used maximum wind speed, mean wind speed, maximum temperature, minimum temperature, mean temperature, maximum sea surface pressure, minimum sea surface pressure, mean sea surface pressure, mean vapor pressure, total rainfall, maximum cloudiness, mean cloudiness, maximum humidity, minimum humidity, mean humidity, sunshine hours, evaporation, mean dew point temperature, mean wet point temperature, maximum air pressure, minimum air pressure, mean air pressure, and mean vapor saturation as input variables. Maximum and mean temperature; maximum wind speed; maximum, minimum, and mean sea surface pressure; maximum, minimum, and mean air pressure; mean vapor pressure; mean cloudiness; mean humidity; sunshine hours; mean dew point temperature; mean wet point temperature; and mean vapor saturation pressure were identified as significant input variables by GT in five or more of the eight studied stations. Also, mean air pressure, mean cloudiness, and mean temperature were identified as significant input variables for Rs modeling by the PA method for more than four stations. Results indicated that although ANN-GT and SVM-GT showed better goodness-of-fit metrics, ANN-PA and SVM-PA had lower uncertainties for estimating Rs. According to the obtained results, almost all models showed that the higher the bandwidth (95PPU or P-factor), the greater the d-factor, and the lower the bandwidth, the lower the d-factor, SVM-PA has the lowest uncertainty among the four models. So, it can be seen that the lowest bandwidth also belonged to the SVM-PA model for Kiashahr with a P-factor of 0.8% and a d-factor of 0.06, although the Aliabad-E-Katoul had the lowest d-factor of 0.017 and a p-factor of 1%. The highest d-factor belonged to the ANN-GT model for a Bandar-E-Torkman with a d-factor of 0.817 and a p-factor of 76%. One reason for the high uncertainty in this model might be due to the number of input variables selected by the GT. Lower uncertainty is a major scale for choosing the optimal model for solving a given problem, suggesting results of the SVM-PA model with lower uncertainty are more reliable.

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References

  • Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007) Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol 333(2–4):413–430

    Article  Google Scholar 

  • Adnan RM, Liang Z, Yuan X, Kisi O, Akhlaq M, Li B (2019a) Comparison of LSSVR, M5RT, NF-GP and NF-SC models for hourly wind speed and wind power prediction based on cross-validation. Energies 12(2):329

    Article  Google Scholar 

  • Adnan RM, Liang Z, Heddam S, Zounemat-Kermani M, Kisi O (2019b) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. J Hydrol 124371

  • Adnan RM, Malik A, Kumar A, Parmar KS, Kisi O (2019c) Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arab J Geosci 12(20):606

    Article  Google Scholar 

  • Aghelpour P, Mohammadi B, Biazar SM (2019) Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theoretical Appl Climatol:1–10

  • Ahmadi A, Han D, Karamouz M, Remesan R (2009) Input data selection for solar radiation estimation. Hydrological Processes: An Int J 23(19):2754–2764

    Article  Google Scholar 

  • Antonopoulos VZ, Papamichail DM, Aschonitis VG, Antonopoulos AV (2019) Solar radiation estimation methods using ANN and empirical models. Comput Electron Agric 160:160–167

    Article  Google Scholar 

  • Ashrafzadeh A, Malik A, Jothiprakash V, Ghorbani MA, Biazar SM (2018) Estimation of daily pan evaporation using neural networks and meta-heuristic approaches. ISH J Hydraulic Eng:1–9

  • Ashrafzadeh A, Ghorbani MA, Biazar SM, Yaseen ZM (2019) Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm. Hydrol Sci J 64(15):1843–1856

    Article  Google Scholar 

  • Ashrafzadeh, A, Kisi, O, Aghelpor, P, Biazar, S.M, Askarizad, M. (2020). Comparative study of time series models, support vector machines, and GMDH in forecasting long-term evapotranspiration rates in northern Iran, J Irrig Drain Eng, Vol. 146, Issue 6. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001471, 04020010

  • Assi, A. H., Al-Shamisi, M. H., Hejase, H. A., & Haddad, A. (2013). Prediction of global solar radiation in UAE using artificial neural networks. In 2013 International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 196–200). IEEE.

  • Azadeh A, Maghsoudi A, Sohrabkhani S (2009) An integrated artificial neural networks approach for predicting global radiation. Energy Convers Manag 50(6):1497–1505

    Article  Google Scholar 

  • Benghanem M, Mellit A, Alamri SN (2009) ANN-based modelling and estimation of daily global solar radiation data: a case study. Energy Convers Manag 50(7):1644–1655

    Article  Google Scholar 

  • Biazar SM, Dinpashoh Y, Singh VP (2019) Sensitivity analysis of the reference crop evapotranspiration in a humid region. Environ Sci Pollut Res:1–28

  • Biazar SM, Ferdosi FB (2020) An investigation on spatial and temporal trends in frost indices in Northern Iran. Theor Appl Climatol. https://doi.org/10.1007/s00704-020-03248-7

  • Bray M, Han D (2004) Identification of support vector machines for runoff modelling. J Hydroinf 6(4):265–280

    Article  Google Scholar 

  • Charalambous C (1992) Conjugate gradient algorithm for efficient training of artificial neural networks. IEE Proceedings G (Circuits, Devices and Systems) 139(3):301–310

    Article  Google Scholar 

  • Chen JL, Li GS (2014) Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables. Theor Appl Climatol 115(3–4):627–638

    Article  Google Scholar 

  • Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An Ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651:2087–2096

    Article  Google Scholar 

  • Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257

    Article  Google Scholar 

  • Deo RC, Ghorbani MA, Samadianfard S, Maraseni T, Bilgili M, Biazar M (2018) Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew Energy 116:309–323

    Article  Google Scholar 

  • Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15(3):208–216

    Article  Google Scholar 

  • Dinpashoh Y, Fakheri-Fard A, Moghaddam M, Jahanbakhsh S, Mirnia M (2004) Selection of variables for the purpose of regionalization of Iran's precipitation climate using multivariate methods. J Hydrol 297(1–4):109–123

    Article  Google Scholar 

  • Dinpashoh, Y., Singh, V. P., Biazar, S. M., & Kavehkar, S. (2019). Impact of climate change on streamflow timing (case study: Guilan Province). Theoretical and Applied Climatology, 1–12.

  • Donatelli M, Carlini L, Bellocchi G (2006) A software component for estimating solar radiation. Environ Model Softw 21(3):411–416

    Article  Google Scholar 

  • Durrant PJ (2001) winGamma: a non-linear data analysis and modelling tool with applications to flood prediction. Unpublished PhD thesis, Department of Computer Science, Cardiff University, Wales, UK.

  • Evans D, Jones AJ (2002) A proof of the Gamma test. Proceedings of the Royal Society of London. Series A: Mathematical. Phys Eng Sci 458(2027):2759–2799

    Article  Google Scholar 

  • Fan J, Wu L, Zhang F, Cai H, Ma X, Bai H (2019) Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China. Renew Sust Energ Rev 105:168–186

    Article  Google Scholar 

  • Fombellida M, Destiné J (1992) The extended quickprop. Artif. Neural Networks:973–977. North-Holland. https://doi.org/10.1016/B978-0-444-89488-5.50032-4

  • Ghorbani MA, Zadeh HA, Isazadeh M, Terzi O (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ Earth Sci 75(6):476

    Article  Google Scholar 

  • Guermoui M, Gairaa K, Rabehi A, Djafer D, Benkaciali S (2018) Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate. Eur Phys J Plus 133(6):211

    Article  Google Scholar 

  • Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993

    Article  Google Scholar 

  • Hook JE, McClendon RW (1992) Estimation of solar radiation data missing from long-term meteorological records. Agron J 84(4):739–742

    Article  Google Scholar 

  • Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417–441

    Article  Google Scholar 

  • Isazadeh M, Biazar SM, Ashrafzadeh A (2017) Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters. Environ Earth Sci 76(17):610

    Article  Google Scholar 

  • Jacobs RA (1988) Increased rates of convergence through learning rate adaptation. Neural Netw 1(4):295–307

    Article  Google Scholar 

  • Jahani, B., & Mohammadi, B. (2018). A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theoretical and Applied Climatology, 1–13.

  • Jamil B, Akhtar N (2017) Estimation of diffuse solar radiation in humid-subtropical climatic region of India: comparison of diffuse fraction and diffusion coefficient models. Energy 131:149–164

    Article  Google Scholar 

  • Johnson GL, Hanson CL (1995) Topographic and atmospheric influences on precipitation variability over a mountainous watershed. J Appl Meteorol 34(1):68–87

    Article  Google Scholar 

  • Jong RD, Stewart DW (1993) Estimating global solar radiation from common meteorological observations in western Canada. Can J Plant Sci 73(2):509–518

    Article  Google Scholar 

  • Khaledian MR, Isazadeh M, Biazar SM, Pham QB (2020) Simulating Caspian Sea surface water level by artificial neural network and support vector machine models. Acta Geophysica:1–11

  • Kisi Ö, Yildirim G (2005) Discussion of “forecasting of reference evapotranspiration by artificial neural networks” by Slavisa Trajkovic, Branimir Todorovic, and Miomir Stankovic. J Irrig Drain Eng 131(4):390–391

    Article  Google Scholar 

  • Krzanowski WJ (1987) Selection of variables to preserve multivariate data structure, using principal components. J R Stat Soc: Ser C: Appl Stat 36(1):22–33

    Google Scholar 

  • Lagos-Avid MP, Bonilla CA (2017) Predicting the particle size distribution of eroded sediment using artificial neural networks. Sci Total Environ 581:833–839

    Article  Google Scholar 

  • Li DH, Chen W, Li S, Lou S (2019a) Estimation of hourly global solar radiation using multivariate adaptive regression spline (MARS)—a case study of Hong Kong. Energy 186:115857

    Article  Google Scholar 

  • Li S, Kazemi H, Rockaway TD (2019b) Performance assessment of stormwater GI practices using artificial neural networks. Sci Total Environ 651:2811–2819

    Article  Google Scholar 

  • Lopez G, Batlles FJ, Tovar-Pescador J (2005) Selection of input parameters to model direct solar irradiance by using artificial neural networks. Energy 30(9):1675–1684

    Article  Google Scholar 

  • Marzo A, Trigo-Gonzalez M, Alonso-Montesinos J, Martínez-Durbán M, López G, Ferrada P, Batlles FJ (2017) Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation. Renew Energy 113:303–311

    Article  Google Scholar 

  • Mercado LM, Bellouin N, Sitch S, Boucher O, Huntingford C, Wild M, Cox PM (2009) Impact of changes in diffuse radiation on the global land carbon sink. Nature 458(7241):1014–1017

    Article  Google Scholar 

  • Mohammadi AA, Yousefi M, Soltani J, Ahangar AG, Javan S (2018) Using the combined model of gamma test and neuro-fuzzy system for modeling and estimating lead bonds in reservoir sediments. Environ Sci Pollut Res 25(30):30315–30324

    Article  Google Scholar 

  • Mohandes MA (2012) Modeling global solar radiation using particle swarm optimization (PSO). Sol Energy 86(11):3137–3145

    Article  Google Scholar 

  • Mohandes, M., Rehman, S., & Halawani, T. O. (1998). Estimation of global solar radiation using artificial neural networks. Renew Energy, 14(1–4), 179–184, 184.

  • Naganna SR, Deka PC, Ghorbani MA, Biazar SM, Al-Ansari N, Yaseen ZM (2019) Dew point temperature estimation: application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water 11:742

    Article  Google Scholar 

  • Nam W, Shin H, Jung Y, Joo K, Heo JH (2015) Delineation of the climatic rainfall regions of South Korea based on a multivariate analysis and regional rainfall frequency analyses. Int J Climatol 35(5):777–793

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Nazari-Sharabian M, Taheriyoun M, Ahmad S, Karakouzian M, Ahmadi A (2019) Water quality modeling of Mahabad Dam watershed–reservoir system under climate change conditions, using SWAT and system dynamics. Water 11(2):394

    Article  Google Scholar 

  • Nazari-Sharabian M, Taheriyoun M, Karakouzian M (2020) Sensitivity analysis of the DEM resolution and effective parameters of runoff yield in the SWAT model: a case study. J Water Supply Res Technol AQUA 69(1):39–54

    Article  Google Scholar 

  • Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Gousheh MG (2011) Assessment of input variables determination on the SVM model performance using PCA, gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 401(3–4):177–189

    Article  Google Scholar 

  • Notton G, Voyant C, Fouilloy A, Duchaud JL, Nivet ML (2019) Some applications of ANN to solar radiation estimation and forecasting for energy applications. Appl Sci 9(1):209

    Article  Google Scholar 

  • Parsaie A, Azamathulla HM, Haghiabi AH (2017) Physical and numerical modeling of performance of detention dams. J Hydrol 121757

  • Perdigão J, Salgado R, Magarreiro C, Soares PM, Costa MJ, Dasari HP (2017) An Iberian climatology of solar radiation obtained from WRF regional climate simulations for 1950–2010 period. Atmos Res 198:151–162

    Article  Google Scholar 

  • Rabehi A, Guermoui M, Lalmi D (2020) Hybrid models for global solar radiation prediction: a case study. Int J Ambient Energy 41(1):31–40

    Article  Google Scholar 

  • Rashidi S, Vafakhah M, Lafdani EK, Javadi MR (2016) Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arab J Geosci 9(11):583

    Article  Google Scholar 

  • Remesan R, Shamim MA, Han D (2008) Model data selection using gamma test for daily solar radiation estimation. Hydrol Process 22(21):4301–4309

    Article  Google Scholar 

  • Richardson CW (1985) Weather simulation for crop management models. Trans ASAE 28(5):1602–1606

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Cognitive Model 5(3):1

    Google Scholar 

  • Samadianfard S, Majnooni-Heris A, Qasem SN, Kisi O, Shamshirband S, Chau KW (2019) Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate. Eng Appl Comput Fluid Mech 13(1):142–157

    Google Scholar 

  • Seifi A, Riahi H (2018) Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran. J Water Clim Change 11:217–240. https://doi.org/10.2166/wcc.2018.003

    Article  Google Scholar 

  • Şenkal O, Kuleli T (2009) Estimation of solar radiation over Turkey using artificial neural network and satellite data. Appl Energy 86(7–8):1222–1228

    Article  Google Scholar 

  • Shamshirband S, Mohammadi K, Khorasanizadeh H, Yee L, Lee M, Petković D, Zalnezhad E (2016) Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model. Renew Sust Energ Rev 56:428–435

    Article  Google Scholar 

  • Singh A, Malik A, Kumar A, Kisi O (2018) Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test. Arab J Geosci 11(11):261

    Article  Google Scholar 

  • Sözen A, Arcaklioǧlu E, Özalp M, Kanit EG (2004) Use of artificial neural networks for mapping of solar potential in Turkey. Appl Energy 77(3):273–286

    Article  Google Scholar 

  • Tian J, Li C, Liu J, Yu F, Cheng S, Zhao N, Wan Jaafar W (2016) Groundwater depth prediction using data-driven models with the assistance of gamma test. Sustainability 8(11):1076

    Article  Google Scholar 

  • Vakili M, Sabbagh-Yazdi SR, Khosrojerdi S, Kalhor K (2017) Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data. J Clean Prod 141:1275–1285

    Article  Google Scholar 

  • Vapnik V, Golowich SE, Smola AJ (1997) Support vector method for function approximation, regression estimation and signal processing. In: Advances in neural information processing systems, pp 281–287. https://doi.org/10.5555/2998981.2999021

    Chapter  Google Scholar 

  • Wang L, Lu Y, Zou L, Feng L, Wei J, Qin W, Niu Z (2019) Prediction of diffuse solar radiation based on multiple variables in China. Renew Sust Energ Rev 103:151–216

    Article  Google Scholar 

  • Xu X, Du H, Zhou G, Mao F, Li P, Fan W, Zhu D (2016) A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products. Energy 111:117–125

    Article  Google Scholar 

  • Yang K, Koike T, Ye B (2006) Improving estimation of hourly, daily, and monthly solar radiation by importing global data sets. Agric For Meteorol 137(1–2):43–55

    Article  Google Scholar 

  • Zajaczkowski J, Wong K, Carter J (2013) Improved historical solar radiation gridded data for Australia. Environ Model Softw 49:64–77

    Article  Google Scholar 

  • Zang H, Cheng L, Ding T, Cheung KW, Wang M, Wei Z, Sun G (2019) Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China. Renew Energy 135:984–1003

    Article  Google Scholar 

  • Zhang J, Zhao L, Deng S, Xu W, Zhang Y (2017) A critical review of the models used to estimate solar radiation. Renew Sust Energ Rev 70:314–329

    Article  Google Scholar 

  • Zinchenko TD, Shitikov VK, Golovatyuk LV, Gusakov VA, Lazareva VI (2019) Analysis of relations between communities of hydrobionts in saline rivers by multidimensional block ordination. Inland Water Biol 12(2):104–110

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the Iran Meteorological Organization for providing data used in this study.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Seyed Mostafa Biazar, Mohammad Isazadeh & Yagob Dinpashoh

  2. Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, 66503, USA

    Vahid Rahmani

  3. School of Technology, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

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  1. Seyed Mostafa Biazar
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  2. Vahid Rahmani
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  3. Mohammad Isazadeh
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  4. Ozgur Kisi
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Correspondence to Ozgur Kisi.

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Biazar, S.M., Rahmani, V., Isazadeh, M. et al. New input selection procedure for machine learning methods in estimating daily global solar radiation. Arab J Geosci 13, 431 (2020). https://doi.org/10.1007/s12517-020-05437-0

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