Abstract
As a primary input in meteorology, the accuracy of solar radiation simulations affects hydrological, climatological, and agricultural studies and sustainable development practices and plans. With the advent of machine learning models and their proven capabilities in modelling the hydro-meteorological phenomena, it is necessary to find the best model suitable for each phenomenon. Models performance depends upon their structure and the input data set. Therefore, some well-known and newest machine learning models with different inputs are tested here for solar radiation simulation in Illinois, USA. The data mining models of Support Vector Machine (SVM), Gene Expression Programming (GEP), Long Short-Term Memory (LSTM), and their combination with the wavelet transformation building a total of six model structures are applied to five data sets to examine their suitability for solar radiation simulation. The five input data sets (SCN_1 to SCN_5) are based on five readily accessible parameters, namely extraterrestrial radiation (Ra), maximum and minimum air temperature (Tmin, Tmax), corrected clear-sky solar irradiation (ICSKY), and Day of Year (DOY). The LSTM outperformed other models, consulting the performance measures of RMSE, SI, MAE, SSRMSE, and SSMAE. Of the different input data sets, in general, SCN_4 was the best input scenario for predicting global daily solar radiation using Ra, Tmax, Tmin, and DOY variables. Overall, six machine learning based models showed acceptable performances for estimating solar radiation, with the LSTM machine learning technique being the most recommended.
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References
Ahmed AAM, Deo RC, Ghahramani A et al (2021) LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stoch Env Res Risk Assess 35:1851–1881. https://doi.org/10.1007/s00477-021-01969-3
Anshuka A, Chandra R, Buzacott AJ, v, et al (2022) Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model. Stoch Env Res Risk Assess. https://doi.org/10.1007/s00477-022-02204-3
Assi AH, Al-Shamisi MH, Hejase HAN, Haddad A (2013) Prediction of global solar radiation in UAE using artificial neural networks. 2013 International Conference on Renewable Energy Research and Applications (ICRERA)
Ferreira C (2002) Gene expression programming in problem solving. Soft computing and industry. Springer, London, pp 635–653
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems
Fortin JG, Anctil F, Parent LÉ, Bolinder MA (2008) Comparison of empirical daily surface incoming solar radiation models. Agric for Meteorol 148:1332–1340. https://doi.org/10.1016/j.agrformet.2008.03.012
Fung KF, Huang YF, Koo CH, Mirzaei M (2020) Improved svr machine learning models for agricultural drought prediction at downstream of langat river basin, Malaysia. J Water Clim Change 11:1383–1398. https://doi.org/10.2166/wcc.2019.295
Galavi H, Mirzaei M, Shui LT, Valizadeh N (2013) Klang River-level forecasting using ARIMA and ANFIS models. J Am Water Works Assoc 105:81–82. https://doi.org/10.5942/jawwa.2013.105.0106
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kaboudan M (2005) Extended daily exchange rates forecasts using wavelet temporal resolutions. New Math Nat Comput 01:79–107. https://doi.org/10.1142/s1793005705000056
Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8. https://doi.org/10.1016/j.cageo.2012.11.015
Khosravi A, Koury RNN, Machado L, Pabon JJG (2018) Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms. J Clean Prod 176:63–75. https://doi.org/10.1016/j.jclepro.2017.12.065
Kim S, Seo Y, Rezaie-Balf M et al (2019) Evaluation of daily solar radiation flux using soft computing approaches based on different meteorological information: peninsula vs continent. Theoret Appl Climatol 137:693–712. https://doi.org/10.1007/s00704-018-2627-x
Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4:87–112. https://doi.org/10.1007/BF00175355
Landeras G, López JJ, Kisi O, Shiri J (2012) Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Convers Manage 62:1–13. https://doi.org/10.1016/j.enconman.2012.03.025
Le Ho, Lee J (2019) Application of long short-term memory (LSTM) neural network for flood forecasting. Water (basel) 11:1387. https://doi.org/10.3390/w11071387
Li MF, Fan L, Liu H et al (2013) A general model for estimation of daily global solar radiation using air temperatures and site geographic parameters in Southwest China. J Atmos Solar Terr Phys 92:145–150. https://doi.org/10.1016/j.jastp.2012.11.001
Lian CY, Huang YF, Ng JL et al (2020) A proposed hybrid rainfall simulation model: Bootstrap aggregated classification tree–artificial neural network (BACT-ANN) for the Langat river basin, Malaysia. J Water Clim Change 11:1218–1234. https://doi.org/10.2166/wcc.2019.294
Alizamir M et al (2020) A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: case studies of the USA and Turkey regions. Energy 197:117239
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693. https://doi.org/10.1109/34.192463
Mesbah M, Soroush E, Rostampour Kakroudi M (2017) Predicting physical properties (viscosity, density, and refractive index) of ternary systems containing 1-octyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide, esters and alcohols at 298.15 K and atmospheric pressure, using rigorous classification techniques. J Mol Liq 225:778–787. https://doi.org/10.1016/j.molliq.2016.11.004
Ming T, de Richter R, Liu W, Caillol S (2014) Fighting global warming by climate engineering: Is the Earth radiation management and the solar radiation management any option for fighting climate change. Renew Sustain Energy Rev 31:792–834
Mirzaei M, Yu H, Dehghani A et al (2021) A novel stacked long short-term memory approach of deep learning for streamflow simulation. Sustainability 13:13384. https://doi.org/10.3390/su132313384
Mohammadi K, Shamshirband S, Anisi MH et al (2015) Support vector regression based prediction of global solar radiation on a horizontal surface. Energy Convers Manage 91:433–441. https://doi.org/10.1016/j.enconman.2014.12.015
Mohanty S, Patra PK, Sahoo SS (2016) Prediction and application of solar radiation with soft computing over traditional and conventional approach - a comprehensive review. Renew Sustain Energy Rev 56:778–796
Mohsenzadeh Karimi S, Karimi S, Poorrajabali M (2021) Forecasting monthly streamflows using heuristic models. ISH J Hydraul Eng 27:73–78. https://doi.org/10.1080/09715010.2018.1516575
Mohsenzadeh Karimi S, Kisi O, Porrajabali M et al (2020) Evaluation of the support vector machine, random forest and geo-statistical methodologies for predicting long-term air temperature. ISH J Hydraul Eng 26:376–386. https://doi.org/10.1080/09715010.2018.1495583
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I - a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nourani V, Khodkar K, Paknezhad NJ, Laux P (2022) Deep learning-based uncertainty quantification of groundwater level predictions. Stoch Env Res Risk Assess. https://doi.org/10.1007/s00477-022-02181-7
Partal T, Kişi Ö (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212. https://doi.org/10.1016/j.jhydrol.2007.05.026
Rohani A, Taki M, Abdollahpour M (2018) A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renewable Energy 115:411–422. https://doi.org/10.1016/j.renene.2017.08.061
Samuel Chukwujindu N (2017) A comprehensive review of empirical models for estimating global solar radiation in Africa. Renew Sustain Energy Rev 78:955–995
Shafaei M, Adamowski J, Fakheri-Fard A et al (2016) A wavelet-SARIMA-ANN hybrid model for precipitation forecasting. J Water Land Dev 28:27–36. https://doi.org/10.1515/jwld-2016-0003
Sharifi SS, Rezaverdinejad V, Nourani V (2016) Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches. J Atmos Solar Terr Phys 149:131–145. https://doi.org/10.1016/j.jastp.2016.10.008
Tymvios FS, Jacovides CP, Michaelides SC, Scouteli C (2005) Comparative study of Ångström’s and artificial neural networks’ methodologies in estimating global solar radiation. Sol Energy 78:752–762. https://doi.org/10.1016/j.solener.2004.09.007
Valizadeh N, Mirzaei M, Allawi MF et al (2017) Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art. Nat Hazards 86:1377–1392. https://doi.org/10.1007/s11069-017-2740-7
Vapnik VN (2000) The nature of statistical learning theory. Springer, New York
Wang J, Xu W, Dong J, Zhang Y (2022) Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning. Stoch Env Res Risk Assess. https://doi.org/10.1007/s00477-022-02202-5
Wang W, Ding J (2003) Wavelet Network Model and Its Application to the Prediction of Hydrology Wavelet Transform Method for Synthetic Generation of Daily Streamflow View project Wavelet Network Model and Its Application to the Prediction of Hydrology. Wang and Ding
Yacef R, Benghanem M, Mellit A (2012) Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study. Renew Energy 48:146–154. https://doi.org/10.1016/j.renene.2012.04.036
Zhao N, Zeng X, Han S (2013) Solar radiation estimation using sunshine hour and air pollution index in China. Energy Convers Manage 76:846–851. https://doi.org/10.1016/j.enconman.2013.08.037
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This work has been possible by the funding provided by the University of Malaya(UM) under the Grant No. GPF049B-2020.
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Mohsenzadeh Karimi, S., Mirzaei, M., Dehghani, A. et al. Hybrids of machine learning techniques and wavelet regression for estimation of daily solar radiation. Stoch Environ Res Risk Assess 36, 4255–4269 (2022). https://doi.org/10.1007/s00477-022-02261-8
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DOI: https://doi.org/10.1007/s00477-022-02261-8