Artificial intelligence for predicting solar still production and comparison with stepwise regression under arid climate

Research Article| January 30 2017 Artificial intelligence for predicting solar still production and comparison with stepwise regression under arid climate Ahmed F. Mashaly; Ahmed F. Mashaly 1Alamoudi Water Research Chair, King Saud University, Riyadh, Saudi Arabia E-mail: mashaly.ahmed@gmail.com Search for other works by this author on: This Site PubMed Google Scholar A. A. Alazba A. A. Alazba 1Alamoudi Water Research Chair, King Saud University, Riyadh, Saudi Arabia2Agricultural Engineering Department, King Saud University, Riyadh, Saudi Arabia Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2017) 66 (3): 166–177. https://doi.org/10.2166/aqua.2017.046 Article history Received: May 24 2016 Accepted: November 20 2016 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Ahmed F. Mashaly, A. A. Alazba; Artificial intelligence for predicting solar still production and comparison with stepwise regression under arid climate. Journal of Water Supply: Research and Technology-Aqua 1 May 2017; 66 (3): 166–177. doi: https://doi.org/10.2166/aqua.2017.046 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Forecasting the efficiency of solar still production (SSP) can reduce the capital risks involved in a solar desalination project. Solar desalination is an attractive method of water desalination and offers a more reliable water source. In this study, to estimate SSP, we employed the data obtained from experimental fieldwork. SSP is assumed to be a function of ambient temperature, relative humidity, wind speed, solar radiation, feed flow rate, temperature of feed water, and total dissolved solids in feed water. In this study, back-propagation artificial neural network (ANN) models with two transfer functions were adopted for predicting SSP. The best performance was obtained by the ANN model with one hidden layer having eight neurons which employed the hyperbolic transfer function. Results of the ANN model were compared with those of stepwise regression (SWR) model. ANN model produced more accurate results compared to SWR model in all modeling stages. Mean values for the coefficient of determination and root mean square error by ANN model were 0.960 and 0.047 L/m2/h, respectively. Relative errors of predicted SSP values by ANN model were about ±10%. In conclusion, the ANN model showed greater potential in accurately predicting SSP, whereas the SWR model showed poor performance. artificial neural network, modeling, solar still production, stepwise regression © IWA Publishing 2017 You do not currently have access to this content.