Abstract
The quality of water is considered as a vital quality factor that affects the life quality in different areas. Thus, its assessment and forecasting became an essential subject matter for several researches. Analytically, the common adopted feed forward error back propagation neural network technique is used to developed two types of ANN models. The first model is applied having experimental data that gotten from the Department of Environment, Basrah-Iraq, during year 2009-2014 and the data set for the current year (2019), while the second model is applied according to the data for the current year (2019) only. The parameters of input of the neural network are pH value (pH), electrical conductivity (EC), Total Dissolved Solid (TDS), Calcium (Ca), Magnesium (Mg), nitrate nitrogen (NO3), phosphorous (PO4 −3) and Sulfate (SO4 −2), the output parameter of the neural network is dissolved oxygen (DO). Through comparing the outcomes of ANN models depending on high value of regressions coefficient (R2) and lower value of mean square error (MSE). For the first model R = 0.96143 and MSE=0.00125 for testing. The second model is R=0.99225 and MSE=0.00532. The results show the proposed ANN prediction model has a great potential significance for the assessment and forecasting the dissolved oxygen.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
