Comparison of ANN, MVR, and SWR models for computing thermal efficiency of a solar still

References

• Abdullah, A.S. 2013. Improving the performance of stepped solar still. Desalination 319:60–65.
   Web of Science ®Google Scholar
• Abutaleb, A.S. 1991. A neural network for the estimation of forces acting on radar targets. Neural Networks 4:667–678.
   Web of Science ®Google Scholar
• Ahsan A., K.M.S. Islam, T. Fukuhara, and A.H. Ghazali. 2010. Experimental study on evaporation, condensation and production of a new tubular solar still. Desalination 260(1–3):172–9.
   Web of Science ®Google Scholar
• Alazba, A. A., M. A. Mattar, M. N. ElNesr, and M. T. Amin. 2012. Field assessment of friction head loss and friction Correction factor equations. Journal of Irrigation and Drainage Engineering 138:166–176.
   Web of Science ®Google Scholar
• Arulsundar, N., N. Subramanian, and R.S.R. Murth. 2005. Comparison of artificial neural networks and multiple liner regression in optimization of formulation parameters of leuprlide acetate loaded leptosomes. Journal of Pharmaceutical Sciences 8:243–258.
   Web of Science ®Google Scholar
• Arunkumar, T., R. Jayaprakash, D. Denkenberger, A. Amimul, M. S. Okundamiya, T. S. k. Hiroshi, and H.Ş. Aybar. 2012. An experimental study on a hemispherical solar still. Desalination 286:342–348.
   Web of Science ®Google Scholar
• Bakirci, K. (2009). Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey. Energy 34:485–501.
   Web of Science ®Google Scholar
• Camargo, A.P., and P.C. Sentelhas.1997. Performance Evaluation of Different Potential Evapotranspiration Estimating Methods in the State of São Paulo, Brazil. Revista Brasileira de Agrometeorologia5:89–97.
   Google Scholar
• Card, D.H., D.L. Peterson, and P.A. Matson. 1988. Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy. Remote Sensing of Environment 26(2):123–147.
   Web of Science ®Google Scholar
• Citakoglu, H. 2015. Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation. Computers and Electronics in Agriculture 118:28–37.
   Web of Science ®Google Scholar
• Cox, W.M., and J.A. Tikvart. 1990. A statistical procedure for determining the best performing air quality simulation model. Atmospheric Environment. 24A:2387–2395.
   Web of Science ®Google Scholar
• Dawson, W.C., and R. Wilby. 1998. An artificial neural networks approach to rainfall-runoff modelling. Hydrological Sciences Journal 43 (1):47–66.
   Web of Science ®Google Scholar
• El-Sebaii A., A. Al-Ghamdi, F. Al-Hazmi, and A. S. Faidah. 2009. Thermal performance of a single basin solar still with PCM as a storage medium. Applied Energy 86(7–8):1187–95.
   Web of Science ®Google Scholar
• Facão, J., S. Varga. and A. C. Oliveira.2004. Evaluation of the use of artificial neural networks for the simulation of hybrid solar collectors. International Journal of green Greenenergy1(3):337–352.
   Google Scholar
• Gnanadason, M. K., P. S. Kumar, G. Sivaraman, and J. E. S. Daniel. 2011. Design and performance analysis of a modified vacuum single basin solar still. Smart Grid and Renewable Energy2(4):388–395.
   Google Scholar
• Hagan, M. T., H.B. Demuth, and M.H. Beale. 1996. Neural Network Design, Boston, Massachusetts: PWS Publishing Company.
   Google Scholar
• Haykin, S. 1999. Neural Networks. A Comprehensive Foundation. New Jersey: Prentice Hall International Inc.
   Google Scholar
• Kabeel, A.E., M.H. Hamed, and Z.M. Omara.2012. Augmentation of the basin type solar still using photovoltaic powered turbulence system. Desalination and Water Treatment 48:182–190.
   Web of Science ®Google Scholar
• Kalogirou, S.A. 2006. Prediction of flat-plate collector performance parameters using artificial neural network. Solar Energy 80:248–259.
   Web of Science ®Google Scholar
• Legates, D.R., and J. McCabe. 1999. Evaluating the use of “goodness-of fit” measures in hydrologic and hydro climatic model validation. Water Resources Research 35:233–241.
   Web of Science ®Google Scholar
• Loague, K., and R.E. Green. 1991. Statistical and graphical methods for evaluating solute transport models: Overview and application. Journal of Contaminant Hydrology 7:51–73.
   Google Scholar
• Mahdi, J.T., B.E. Smith, and A.O. Sharif. 2011. An experimental wick-type solar still system: Design and construction. Desalination 267:233–238.
   Web of Science ®Google Scholar
• Mashaly, A. F., and A. A. Alazba. 2015. Comparative investigation of artificial neural network learning algorithms for modeling solar still production. Journal of Water Reuse and Desalination5(4): 480–493.
   Web of Science ®Google Scholar
• Mashaly, A. F., A. A. Alazba, A. M. Al-Awaadh, and M. A. Mattar. 2015. Predictive model for assessing and optimizing solar still performance using artificial neural network under hyper arid environment. Solar Energy118:41–58.
   Web of Science ®Google Scholar
• Mashaly, A. F., A. A. Alazba, and A. M. Al-Awaadh. 2016. Assessing the performance of solar desalination system to approach near-ZLD under hyper arid environment. Desalination and Water Treatment 57:12019–12036.
   Web of Science ®Google Scholar
• Mashaly, A. F., and A.A. Alazba. 2016. MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment. Computers and Electronics in Agriculture 122:146–155.
   Web of Science ®Google Scholar
• Mogaji, K. A. 2016. Geoelectrical parameter-based multivariate regression borehole yield model for predicting aquifer yield in managing groundwater resource sustainability. Journal of Taibah University for Science 10:1658–3655.
   Google Scholar
• Nikolopoulos, K., P. Goodwin, A. Patelis, and V. Assimatopoulos. 2007. Forecasting with cue information: A comparison of multiple regressions with alternative forecasting approaches. European Journal of Operational Research 180:354–368.
   Web of Science ®Google Scholar
• Omara, Z.M., A.E. Kabeel, and M.M. Younes. 2013. Enhancing the stepped solar still performance using internal reflectors. Desalination 314:67–72.
   Web of Science ®Google Scholar
• Rahul, D. and G.N. Tiwari. 2009. Characteristic equation of a passive solar still. Desalination245:246–65.
   Web of Science ®Google Scholar
• Rai, P., G. C. Majumdar, S. DasGupta, and S. De. 2005. Prediction of the viscosity of clarified fruit juice using artificial neural network: A combined effect of concentration and temperature. Journal of Food Engineering 68:527–533.
   Web of Science ®Google Scholar
• Sadineni, S.B., R. Hurt, C.K. Halford, and R.F. Boehm. 2008. Theory and experimental investigation of a weir-type inclined solar still. Energy 33(1):71–80.
   Web of Science ®Google Scholar
• Şahin, M., Y. Kaya, and M. Uyar. 2013. Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data. Advances in Space Research 51(5):891–904.
   Web of Science ®Google Scholar
• Santos, N. I., A. M. Said, D. E. James, and N. H. Venkatesh. 2012. Modeling solar still production using local weather data and artificial neural networks. Renewable Energy 40:71–79.
   Web of Science ®Google Scholar
• Singh, A.K., G.N. Tiwari, P.B. Sharma, and E. Khan. 1995. Optimization of orientation for higher yield of solar still for a given location. Energy Conversion and Management 36:175–187.
   Web of Science ®Google Scholar
• Vanclay, J.K., and J.P. Skovsgaard. 1997. Evaluating forest growth models. Ecological Modelling 98:1–12.
   Web of Science ®Google Scholar
• Vesta Services Inc. 2000. Qnet2000 Shareware, Vesta Services, Inc., 1001 Green Bay Rd, STE 196, Winnetka, IL 60093.
   Google Scholar
• Vinoth, K.K., and R. K. Bai. 2008. Performance study on solar still with enhanced condensation. Desalination 230 (1–3):51–61.
   Web of Science ®Google Scholar
• Willmott, C.J. 1981. On the validation of models. Physical Geography 2:184–194.
   Google Scholar
• Yadav, S., and K. Sudhakar. 2015. Different domestic designs of solar stills: A review. Renewable and Sustainable Energy Reviews 47:718–731.
   Google Scholar
• Yaïci, W., and E. Entchev. 2014. Performance prediction of a solar thermal energy system using artificial neural networks. Applied Thermal Engineering 73:1348–1359.
   Web of Science ®Google Scholar
• Zacharias, S., C.D. Heatwole, and C.W. Coakley. 1996. Robust quantitative techniques for validating pesticide transport models. Transactions of the ASAE 39:47–54.
   Google Scholar
• Zanetti, S.S., E.F. Sousa, V.P.S. Oliveira, F.T. Almeida, and S. Bernardo. 2007. Estimating evapotranspiration using artificial neural network and minimum climatological data. Journal of Irrigation and Drainage Engineering—ASCE 133 (2): 83–89.
   Web of Science ®Google Scholar
• Zurada, J. M. 1992. Introduction to Artificial Neural Systems. Saint Paul, Minnesota, West Publishing Company.
   Google Scholar