A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Estimation of groundwater recharge using simulation-optimization model and cascade forward ANN at East Nile Delta aquifer, Egypt
Study region: The Quaternary Aquifer at the Eastern Nile Delta (QAEND), Egypt. Study focus: A novel model is studied to build relationships between the available geomorphological and hydrogeological data, and the unknown time-dependent recharge rates. This is useful in performing inexpensive sustainable simulation and management of the proposed aquifer. Recharge rates in the year 2005 are calibrated using the observed hydraulic heads. The unstructured grid version of MODFLOW (MFUSG) coupled with the Particle Swarm Optimization (PSO) algorithm is used to solve the calibration problem. Then, a multilayer Cascade Forward Artificial Neural Network (CFNN) is trained using the calibrated recharge rates to conclude the required relationships between available data and unknown recharge rates, for any subsequent year. Four different training methods are adopted for CFNN: 1) PSO, 2) Levenberg Marquart (LM), 3) LM in combination with the Bayesian Regularization (BR), and 4) hybrid algorithm between LM - BR and PSO, which is used for the first time in training the CFNN to predict the net recharge rates and is found to be the best one. New hydrological insights for the region: The trained CFNN is used to provide the groundwater flow model, MFUSG, with necessary recharge rates during the transient simulation of the groundwater between 2005 and 2015. Good matching between simulated and observed hydraulic heads is validating the model in forcasting future recgharge rates.
Estimation of groundwater recharge using simulation-optimization model and cascade forward ANN at East Nile Delta aquifer, Egypt
Study region: The Quaternary Aquifer at the Eastern Nile Delta (QAEND), Egypt. Study focus: A novel model is studied to build relationships between the available geomorphological and hydrogeological data, and the unknown time-dependent recharge rates. This is useful in performing inexpensive sustainable simulation and management of the proposed aquifer. Recharge rates in the year 2005 are calibrated using the observed hydraulic heads. The unstructured grid version of MODFLOW (MFUSG) coupled with the Particle Swarm Optimization (PSO) algorithm is used to solve the calibration problem. Then, a multilayer Cascade Forward Artificial Neural Network (CFNN) is trained using the calibrated recharge rates to conclude the required relationships between available data and unknown recharge rates, for any subsequent year. Four different training methods are adopted for CFNN: 1) PSO, 2) Levenberg Marquart (LM), 3) LM in combination with the Bayesian Regularization (BR), and 4) hybrid algorithm between LM - BR and PSO, which is used for the first time in training the CFNN to predict the net recharge rates and is found to be the best one. New hydrological insights for the region: The trained CFNN is used to provide the groundwater flow model, MFUSG, with necessary recharge rates during the transient simulation of the groundwater between 2005 and 2015. Good matching between simulated and observed hydraulic heads is validating the model in forcasting future recgharge rates.
Estimation of groundwater recharge using simulation-optimization model and cascade forward ANN at East Nile Delta aquifer, Egypt
Mahmoud E. Abd-Elmaboud (author) / Hossam A. Abdel-Gawad (author) / Kassem S. El-Alfy (author) / Mohsen M. Ezzeldin (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Environmentally stable isotopes and groundwater recharge in the eastern Nile delta
| Taylor & Francis Verlag | 1987