He was born in year of 1984 from Yasuj, Iran. He recieved his Ph.D in field of civil engineering, Hydraulic division from University of Qom, Iran in 2018. His research field is climate change impacts, modeling quality parameters of surface waters using several novel machines learning for prediction solution. He has published several articles in his interesting field in hydrology top journals. He is now working as a building advisor in the kerman cement industries group located at Tehran, Iran as well. Supervisors: Taher Rajaee Phone: 09120692842
Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s... more Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s u m m a r y The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet–linear genetic programming (WLGP) model for prediction of monthly sodium (Na +) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na + datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na + one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash–Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na + peak values is also presented in this study.
Journal of Experimental & Theoretical Artificial Intelligence, 2015
ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especiall... more ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash-Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.
The prediction of water quality parameters plays an important role in water resources and environ... more The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet-neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R (2)) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.
Journal of Experimental Theoretical Artificial Intelligence, Feb 15, 2015
ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especiall... more ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash-Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.
The streamflows are important and effective factors in stream ecosystems and its accurate predict... more The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations , Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.
The Shah Qasim dam was constructed in 1996 and is located 15 km from south Yasuj city, Iran and h... more The Shah Qasim dam was constructed in 1996 and is located 15 km from south Yasuj city, Iran and having 9 million m3 active volume with a surface area of 670 000 m2. This study evaluates different environmental effects caused by this dam. Wooten and Rau Matrix method has been used to evaluate separately, the physical environmental parameters like micro-climate, air quality, sound, surface water quality and quantity, groundwater quality, the static water level, utilization of surface water, sedimentation, erosion, stability and the soil properties, seismicity effects and the biological environmental parameters such as habitat and herbal effects and socio-economical aspect including population, employment, education, welfare, resettlement, safety and security, cultural and landscapes both in construction and operation periods. The effect of each activity is determined by its importance on that dam. After determining the total effect of these activities, it can be seen that, the Shah Qa...
This study describes the application of Artificial Neural Network (ANN) and Wavelet-Artificial Ne... more This study describes the application of Artificial Neural Network (ANN) and Wavelet-Artificial Neural Network (WANN) models for the prediction of Chloride (Cl-) content of Beshar River at Yasuj, Iran. Both models used 10 years (2002-2012) monthly variations of Shah Mokhtar Gauging Station’s water quality variables (E.C., K+ and Na+) as input parameters. Performance of this models was evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE). Results showed that, WANN model performance was better than ANN model. The identified WANN model can be used as tools for the computation of water quality parameters.
Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surf... more Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash– Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.
The prediction of water quality parameters plays an important role in water resources and environ... more The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet–neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R 2) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.
Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s... more Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s u m m a r y The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet–linear genetic programming (WLGP) model for prediction of monthly sodium (Na +) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na + datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na + one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash–Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na + peak values is also presented in this study.
Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s... more Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s u m m a r y The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet–linear genetic programming (WLGP) model for prediction of monthly sodium (Na +) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na + datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na + one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash–Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na + peak values is also presented in this study.
Journal of Experimental & Theoretical Artificial Intelligence, 2015
ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especiall... more ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash-Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.
The prediction of water quality parameters plays an important role in water resources and environ... more The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet-neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R (2)) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.
Journal of Experimental Theoretical Artificial Intelligence, Feb 15, 2015
ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especiall... more ABSTRACT Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash-Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.
The streamflows are important and effective factors in stream ecosystems and its accurate predict... more The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations , Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.
The Shah Qasim dam was constructed in 1996 and is located 15 km from south Yasuj city, Iran and h... more The Shah Qasim dam was constructed in 1996 and is located 15 km from south Yasuj city, Iran and having 9 million m3 active volume with a surface area of 670 000 m2. This study evaluates different environmental effects caused by this dam. Wooten and Rau Matrix method has been used to evaluate separately, the physical environmental parameters like micro-climate, air quality, sound, surface water quality and quantity, groundwater quality, the static water level, utilization of surface water, sedimentation, erosion, stability and the soil properties, seismicity effects and the biological environmental parameters such as habitat and herbal effects and socio-economical aspect including population, employment, education, welfare, resettlement, safety and security, cultural and landscapes both in construction and operation periods. The effect of each activity is determined by its importance on that dam. After determining the total effect of these activities, it can be seen that, the Shah Qa...
This study describes the application of Artificial Neural Network (ANN) and Wavelet-Artificial Ne... more This study describes the application of Artificial Neural Network (ANN) and Wavelet-Artificial Neural Network (WANN) models for the prediction of Chloride (Cl-) content of Beshar River at Yasuj, Iran. Both models used 10 years (2002-2012) monthly variations of Shah Mokhtar Gauging Station’s water quality variables (E.C., K+ and Na+) as input parameters. Performance of this models was evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE). Results showed that, WANN model performance was better than ANN model. The identified WANN model can be used as tools for the computation of water quality parameters.
Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surf... more Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash– Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.
The prediction of water quality parameters plays an important role in water resources and environ... more The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet–neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R 2) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.
Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s... more Keywords: Water quality Na + concentration Wavelet–linear genetic programming Data driven model s u m m a r y The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet–linear genetic programming (WLGP) model for prediction of monthly sodium (Na +) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na + datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na + one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash–Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na + peak values is also presented in this study.
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Papers by Masoud Ravansalar