Maryam Hafezparast Mavadat; Seiran Marabi
Abstract
Surface water quality is of particular importance due to its drinking, industrial, and agricultural water sources. Changes in rainfall, temperature and river discharge can affect surface water quality. In this study, the effect of CANESM2, FIO, GFDL, MIROC climate models and weight composition model ...
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Surface water quality is of particular importance due to its drinking, industrial, and agricultural water sources. Changes in rainfall, temperature and river discharge can affect surface water quality. In this study, the effect of CANESM2, FIO, GFDL, MIROC climate models and weight composition model of CMIP5 (Coupled Model Intercomparison Project) under representative concentration pathways (RCP) of 4.5, 6, 8.5 scenarios on rainfall and temperature were investigated and then monthly discharge of the Aran river in Iran during 2020-2052 and 2053-2085 is predicted using the IHACRES runoff model. Next, the LSTM (Long Short-Term Memory network)-RNN (Recurrent Neural Networks) model were used to predict the total dissolved solids (TDS), sodium adsorption ratio (SAR) for the period 2020-2030. The results showed that the long-term monthly rainfall under the RCP8.5 scenario reported a further decrease compared to the RCP4.5 and RCP6, and the rainfall fluctuations were higher than the other two scenarios. Temperature changes in the second period are higher than the first period, so that in the first period under the scenarios of RCP4.5, RCP6 and RCP8.5 showed respectively 1, 1.5 and 2 degrees Celsius increase, while in the second period, 2, 3 and 4 degrees Celsius is predicted. The average discharge shows by 15.8 % and 20.97 % respectively decrease under the RCP4.5 scenario in the first and second periods, while by 8.51 % and 27.55 % under the RCP6 scenario and 6.38 % and 39.89 % under the RCP8.5 scenario compared to the observed discharge. The mean, maximum, and minimum TDS parameters under RCP4.5 scenario are, respectively, 295, 410, and 263, and 302, 410, and 257 under RCP6 scenario while 292, 410, and 257 mg, for RCP8.5 scenario. These changes are, respectively, 0.42, 0.93 and 0.14 for the SAR parameter in RCP4.5 scenario, and equal to 0.44, 0.94 and 0.1 in scenario 6, while 0.42, 0.93 and 0.15, respectively, for RCP8.5 scenario in Khorramrood river.
Mazen Hamada; Hossam Adel Zaqoot; Ahmed Abu Jreiban
Abstract
This paper is concerned with the use of artificial neural network and multiple linear regression (MLR) models for the prediction of three major water quality parameters in the Gaza wastewater treatment plant. The data sets used in this study consist of nine years and collected from Gaza wastewater treatment ...
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This paper is concerned with the use of artificial neural network and multiple linear regression (MLR) models for the prediction of three major water quality parameters in the Gaza wastewater treatment plant. The data sets used in this study consist of nine years and collected from Gaza wastewater treatment plant during monthly records. Treatment efficiency of the plant was determined by taking into account of influent input values of pH, temperature (T), biological oxygen demand (BOD), chemical oxygen demand (COD) and total dissolved solids (TSS) with effluent output values of BOD, COD and TSS. Performance of the model was compared via the parameters of root mean squared error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r). The suitable architecture of the neural network model is determined after several trial and error steps. Results showed that the artificial neural network (ANN) performance model was better than the MLR model. It was found that the ANN model could be employed successfully in estimating the BOD, COD and TSS in the outlet of Gaza wastewater treatment plant. Moreover, sensitive examination results showed that influent TSS and T parameters have more effect on BOD, COD and TSS predicting to other parameters.
