ORCID Profile
0000-0002-1902-3099
Current Organisation
UNSW Sydney
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Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2020
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 16-02-2019
DOI: 10.1007/S10661-019-7196-7
Abstract: In this study, artificial neural networks (ANNs) including feed forward back propagation neural network (FFBP-NN) and the radial basis function neural network (RBF-NN) were applied to predict daily sewage sludge quantity in wastewater treatment plant (WWTP). Daily datasets of sewage sludge have been used to develop the artificial intelligence models. Six mother wavelet (W) functions were employed as a preprocessor in order to increase accuracy level of ANNs. In this way, a 4-day lags were considered as input variables to conduct training and testing stages for the proposed W-ANNs. To compare performance of W-ANNs with traditional ANNs, coefficient of correlation (R), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) were considered. In the case of all wavelet functions, it was found that W-FFBP-NN (R = 0.99 and MAE = 5.78) and W-RBF-NN (R = 0.99 and MAE = 6.69) models had superiority to the FFBP-NN (R = 0.9 and MAE = 21.41) and RBF-NN (R = 0.9 and MAE = 20.1) models. Furthermore, the use of DMeyer function to improve ANNs indicated that W-FFBP-NN (RMSE = 7.76 and NSE = 0.98) and W-RBF-NN (RMSE = 9.35 and NSE = 0.98) approaches stood at the highest level of precision in comparison with other mother wavelet functions used to develop the FFBP-NN and RBF-NN approaches. Overall, this study proved that application of various mother wavelet functions into architecture of ANNs led to increasing accuracy of artificial neural networks for estimation of sewage sludge volume in the WWTP.
Publisher: Springer Science and Business Media LLC
Date: 06-2018
DOI: 10.1007/S11356-018-1975-5
Abstract: Determining the quantity of sewage sludge is a major component of designing sludge treatment units and their handling and disposal facilities including its fluctuation over a wide range. In the present study, the capabilities of the hybrid wavelet-gene expression programming (WGEP), wavelet-model tree (WMT), and wavelet-evolutionary polynomial regression (WEPR) models have been investigated to predict the quantity of daily sewage sludge. In the first step, the single gene expression programming (GEP), model tree (MT), and evolutionary polynomial regression (EPR) models were employed to predict the amounts of sewage sludge based on the input vector content produced by the sewage sludge data series, which ranged from lagged-1 day to lagged-4 days. In this study, the WGEP, WMT, and WEPR models were obtained through the combination of two methods: discrete wavelet transforms (DWT) and simple GEP, MT, and EPR models. Incidentally, the models were implemented by transforming the input datasets using the Meyer wavelet function in order to reveal the temporal and spectral information contained within the data, and subsequently, this transformed data was used as the input vectors for the simple GEP, MT, and EPR models. In addition, the results of the wavelet conjunction model were compared with those obtained using the simple GEP, MT, and EPR models. The study indicated that the performance of the wavelet coupled-models was better than the simple models. The quantitative comparisons demonstrated that the WMT, with root mean square error (RMSE) of 8.15 and R = 0.98, performed better than the WGEP (RMSE = 15.26 and R = 0.92) and WEPR (RMSE = 18.20 and R = 0.89) models. Overall, the use of wavelet conjunction models provided an acceptable performance in order to improve precision in one of the most effective parameters involved in the design of a wastewater treatment plant (WWTP).
Publisher: Elsevier BV
Date: 05-2019
Publisher: Copernicus GmbH
Date: 11-12-2015
DOI: 10.5194/ISPRSARCHIVES-XL-1-W5-769-2015
Abstract: Abstract. The purpose of the present study is to use Geographical Information Systems (GISs) for determining the best areas having ground water potential in Baft city. To achieve this objective, parameters such as precipitation, slope, fault, vegetation, land cover and lithology were used. Regarding different weight of these parameters effect, Analytic Hierarchy Process (AHP) was used. After developing informational layers in GIS and weighing each of them, a model was developed. The final map of ground waters potential was calculated through the above-mentioned model. Through applying our developed model four areas having high, average, low potential and without required potential distinguished. Results of this research indicated that 0.74, 41.23 and 45.63 percent of the area had high, average and low potential, respectively. Moreover, 12.38% of this area had no potential. Obtained results can be useful in management plans of ground water resources and preventing excessive exploitation.
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2021
Publisher: Elsevier BV
Date: 08-2023
Location: Iran (Islamic Republic of)
Location: Iran (Islamic Republic of)
Location: Iran (Islamic Republic of)
Location: No location found
No related grants have been discovered for Maryam Zeinolabedini Rezaabad.