JWSS - Journal of Water and Soil Science

Urban and industrial wastewaters are considered as the most contaminant of surface water. Entrance   of these pollutants to the river reduces the concentration of dissolved oxygen and aquatic life will be threatened. So, one of the main qualitative characteristics of water resources management is the concentration of dissolved oxygen. The base of the   developed model in this investigation is the convection- diffusion equation in soil. Terms of production and decay of dissolved oxygen were added to this equation. The final equation was discretized using the finite difference method with the implicit scheme. With applying the initial and boundary conditions, the equation set was solved by the Thomas algorithm. The calculations were done by programming in the MATLAB software. For the calibration and validation of the model, data obtained from two reaches of Zayanderoud River, including steel melt and Mobarakeh Steel factories, were used. The temporal and spatial variations of the dissolved oxygen were plotted and compared with the real data and the results of the MSP and CSP models. The results showed that the concentration of the dissolved oxygen could be well predicted through solving convection-diffusion equation with introducing two terms for the decay and production of oxygen. The comparison between the results of the model and two other models showed that the model led to better results in comparison to the MSP and CSP models.

Nowadays, environmental pollutions especially water pollution is increasingly developing. One of the problems of entering the pollutants to rivers is reduction in the concentration of river dissolved oxygen. In order to manage the water resources, amount of dissolved oxygen should be predicted. This study presents a novel equation for simulating the concentration of river dissolved oxygen by adding the oxygen production and consumption in the river factors to equation for transmission-diffusion of minerals in the soil. The resultant equation was separated in finite differential method and by using implicit pattern. Calculations were done by encodings in MATLAB software. In order to calibrate and confirm the dissolved oxygen model, data derived from Zayanderood River around Zob-Ahan factory of Isfahan and Mobarakeh Steel Complex was used. By using some data, coefficients of model were determined. Analyzing the sensitivity of model coefficients showed that aeration constant (K_r) had the most effect on predicting the model. Since depends on hydraulic parameters of river, sensitivity of depth and pace of river was studied and finally depth of river was introduced as the most sensitive variable.

In this study, we used the ARIMA time series model, the fuzzy-neural inference network, multi-layer perceptron artificial neural network, and ARIMA-ANN, ARIMA-ANFIS hybrid models for the modeling and prediction of the daily electrical conductivity parameter of daily teleZang hydrometric station over the statistical period of 49 years. For this purpose, the daily data for the 1996-2004 period were used for model training and data for the 1996-2006 period were applied for testing. In order to verify the validity of the fitted ARIMA models, the residual autocorrelation and partial autocorrelation functions and Port Manteau statistics were used. PMI algorithm were   then used to model and predict electrical conductivity for selecting the effective input parameter of the neural fuzzy inference network and the artificial neural network. The daily parameters of magnesium (with two days delay) and sodium (with one day delay), heat (with one day delay), flow rate (with two months delay), and acidity (with one day delay) were obtained with the lowest values of Akaike and highest values of hempel statistics as the input of the neural fuzzy inference network and the artificial neural network for modelling daily electric conductivity predictions; then predictions were made. Also, models evaluation criteria confirmed the superiority of the ARIMA-ANFIS hybrid model with the trapezoidal membership function and with two membership numbers, as compared to other models with a coefficient of determination of 0.86 and the root mean square of 29 dS / m. Also, the Arima model had the weakest performance. So, it could be applied to modeling and forecasting the daily quality parameter of the tele Zang hydrometer station.