Nasrin Abozari; Mohammadreza Hassanvand; Amir Hossein Salimi; Salim Heddam; Hossein Omidvar Mohammadi; Amir Noori
Abstract
The frequent occurrences of wet and dry in the catchment area of the Gamasiab river located in the west of Iran, in addition to affecting the quantitative status of surface water, has caused changes in the water quality of the basin. Therefore, modeling and prediction of Gamasiab river water quality ...
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The frequent occurrences of wet and dry in the catchment area of the Gamasiab river located in the west of Iran, in addition to affecting the quantitative status of surface water, has caused changes in the water quality of the basin. Therefore, modeling and prediction of Gamasiab river water quality in wet and dry periods are research priority. In this study, an optimized artificial neural network (ANN) trained with three different optimization algorithms namely; particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA) was proposed for predicting the electric conductivity (EC). For this purpose, water quality data from 1967 to 2017 collected at the hydrometric station in the Gamasiab river were used for developing and testing the models. First, the study program was divided into two periods of wet and dry, this classification based on flow rate in the river. Then, in a preliminary statistical analysis, the effective parameters were determined for EC estimation. The performance of the applied methods showed that the ANN optimized using ICA algorithm was better than the ANN optimized with GA and PSO, and also the standard ANN without optimization. Overall, the ANN optimized with ICA has higher R and lower MARE and RMSE, with values of 11.56, 19.63 and 0.93, during the dry period, and 10.63, 17.19 and 0.97 during the wet period, respectively.
Yahya Choopan; Somayeh Emami
Abstract
In this study, barley yield has been estimated via radial basis function network (RBF) and feed-forward neural networks (GFF) models of artificial neural network (ANNs) in Torbat-Heydarieh of Iran. For this purpose, a dataset consists of 200 data at three levels of irrigation with well water, industrial ...
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In this study, barley yield has been estimated via radial basis function network (RBF) and feed-forward neural networks (GFF) models of artificial neural network (ANNs) in Torbat-Heydarieh of Iran. For this purpose, a dataset consists of 200 data at three levels of irrigation with well water, industrial wastewater (sugar factory wastewater), a combination of well water and wastewater in two levels (complete irrigation and irrigation with 75 % water stress) and soil characteristics of area were used as input parameters. To achieve this goal, based on the number of data and inputs, 200 barley field experiments data set were used, of which 80 % (160 data) was used for the training and 20 % (40 data) for the testing the network. The results showed that RBF model has high potential in estimating barley yield with Levenberg Marquardt training and 4 hidden layers. Also the values of statistical parameters R2 and RMSE were 0.81 and the 33.12, respectively. In general, the results showed that ANNs model is able to better estimate the barley yield when irrigation water level parameter with well water is selected as input.
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.