Open AccessForecasting of Dissolved Oxygen in Shatt Al-Arab River Based on parameters of water quality Using Artificial Neural Networks
Author(s) -
Z A Khudhur,
Ammar Salman Dawood,
Saad Arab
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/888/1/012049
Subject(s) - mean squared error , artificial neural network , water quality , current (fluid) , environmental science , coefficient of determination , statistics , mathematics , hydrology (agriculture) , computer science , engineering , machine learning , geology , ecology , geotechnical engineering , oceanography , biology
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 (NO 3 ), phosphorous (PO 4 −3 ) and Sulfate (SO 4 −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 (R 2 ) 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.