This is a preview of subscription content, log in via an institution to check access.
Data availability
Not applicable.
References
Altunkaynak A, Nigussie TA (2016) Performance comparison of SAS-multilayer perceptron and wavelet-multilayer perceptron models in terms of daily streamflow prediction. J Hydrol Eng 21:1–13. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001263
Anastasovski A (2021) Improvement of energy efficiency in ethanol production supported with solar thermal energy – a case study. J Clean Prod 278:123476. https://doi.org/10.1016/j.jclepro.2020.123476
Angstrom A (1924) Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Q J R Meteorol Soc 50:121–126. https://doi.org/10.1002/qj.49705021008
Bakirci K (2009) Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey. Energy. 34:485–501. https://doi.org/10.1016/j.energy.2009.02.005
Bamehr S, Sabetghadam S (2020) Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran. Environ Sci Pollut Res 28:7167–7179. https://doi.org/10.1007/s11356-020-11003-8
Başakın EE, Ekmekcioğlu Ö, Mohammadi B (2020) Letter to the editor “comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes”. Environ Sci Pollut Res 27:22131–22134. https://doi.org/10.1007/s11356-020-08666-8
Cohen J, Cohen P, West S, Aiken L (2003) Applied multiple regression/correlation analysis for the behavioral sciences. Routledge, New York. https://doi.org/10.4324/9780203774441
Dincer F (2011) The analysis on photovoltaic electricity generation status, potential and policies of the leading countries in solar energy. Renew Sust Energ Rev 15:713–720. https://doi.org/10.1016/j.rser.2010.09.026
Džiugaitė-Tumėnienė R, Motuzienė V, Šiupšinskas G, Čiuprinskas K, Rogoža A (2017) Integrated assessment of energy supply system of an energy-efficient house. Energ Buildings 138:443–454. https://doi.org/10.1016/j.enbuild.2016.12.058
El-Metwally M (2005) Sunshine and global solar radiation estimation at different sites in Egypt. J Atmos Sol Terr Phys 67:1331–1342. https://doi.org/10.1016/j.jastp.2005.04.004
Gayen A, Pourghasemi HR, Saha S, Keesstra S, Bai S (2019) Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci Total Environ 668:124–138. https://doi.org/10.1016/j.scitotenv.2019.02.436
Haukkala T (2015) Does the sun shine in the High North? Vested interests as a barrier to solar energy deployment in Finland. Energy Res Soc Sci 6:50–58. https://doi.org/10.1016/j.erss.2014.11.005
Heiskanen E, Nissilä H, Lovio R (2015) Demonstration buildings as protected spaces for clean energy solutions - the case of solar building integration in Finland. J Clean Prod 109:347–356. https://doi.org/10.1016/j.jclepro.2015.04.090
Jain PC (1986) Global irradiation estimation for Italian locations. Sol Wind Technol 3:323–328. https://doi.org/10.1016/0741-983X(86)90013-5
Jamil B, Bellos E (2019) Development of empirical models for estimation of global solar radiation exergy in India. J Clean Prod 207:1–16. https://doi.org/10.1016/j.jclepro.2018.09.246
Martins F, Felgueiras C, Smitková M (2018) Fossil fuel energy consumption in European countries. Energy Procedia 153:107–111. https://doi.org/10.1016/j.egypro.2018.10.050
Marzouq M, Bounoua Z, El Fadili H et al (2019) New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks. J Clean Prod 209:1105–1118. https://doi.org/10.1016/j.jclepro.2018.10.254
Osborne JW, Waters E (2002) Four assumptions of multiple regression that researchers should always test. Pract Assessment Res Eval 8:1–5. https://doi.org/10.7275/r222-hv23
Rensheng C, Shihua L, Ersi K, Jianping Y, Xibin J (2006) Estimating daily global radiation using two types of revised models in China. Energy Convers Manag 47:865–878. https://doi.org/10.1016/j.enconman.2005.06.015
Seljom P, Lindberg KB, Tomasgard A, Doorman G, Sartori I (2017) The impact of Zero Energy Buildings on the Scandinavian energy system. Energy 118:284–296. https://doi.org/10.1016/j.energy.2016.12.008
Sui J, Chen Z, Wang C et al (2020) Efficient hydrogen production from solar energy and fossil fuel via water-electrolysis and methane-steamreforming hybridization. Appl Energy 276:115409. https://doi.org/10.1016/j.apenergy.2020.115409
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. https://doi.org/10.1016/j.jclepro.2016.09.145
Williams MN, Grajales CAG, Kurkiewicz D (2013) Assumptions of multiple regression: correcting two misconceptions. Pract Assess Res Eval 18:1–14
Zhong W, An H, Shen L, Fang W, Gao X, Dong D (2017) The roles of countries in the international fossil fuel trade: an emergy and network analysis. Energy Policy 100:365–376. https://doi.org/10.1016/j.enpol.2016.07.025
Author information
Authors and Affiliations
Contributions
Eyyup Ensar Başakın (conceptualization, writing—original draft); Ömer Ekmekcioğlu (writing—review and editing)
Corresponding author
Ethics declarations
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent to publish
All the authors have reviewed the final version of the manuscript and mutually agreed that it should be submitted to Environmental Science and Pollution Research.
Conflict of interest
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Garrigues
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Başakın, E.E., Ekmekcioğlu, Ö. Letter to the Editor “Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran”. Environ Sci Pollut Res 28, 19530–19532 (2021). https://doi.org/10.1007/s11356-021-13201-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-13201-4