Recovering the Salinity Distributed Sources Into River from Aquifer Using the Simulation-Optimization Method

Document Type : Research Paper

Authors

1 Engineering and Water Management Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

2 Civil Engineering Department, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Iran.

10.22059/jwim.2023.355061.1051

Abstract

Due to the increase in population and the need for water supply, preservation and protection of surface water and groundwater resources has been considered by governments. One of the pollutant sources in rivers is entering salinity from groundwater into the river, that in this research is considered as distributed (non-point) sources. The goal is to identify the salinity intensity, location and length of sources by measuring the temporal distribution of concentration in one observation point. For this purpose, the inverse solution of advection-dispersion equation in the river was employed using the simulation-optimization approach. MIKE11 numerical model was used to simulate flow and transfer of salinity in the river, and genetic algorithm was employed for optimization. In the proposed model, considering only one observation point with some measured intensity data for recovering several sources, unknown location and length of the sources, in addition to their intensities is the most significant advantage of the present study. The model verified by using hypothetical examples, 40 km section of the Karun River and also by applying five and 15 percent noise to the observation data. The results confirm the ability of the model to recover the specifications of several distributed sources using only one observation point. With five percent of noise in the observation data, all three specifications of sources can be recovered with the desired accuracy. While at 15 percent of noise, the accuracy of the model in recovering the location and length of sources was decreased. Also, to recover the specifications of each source, employing only three points of the measured data in the ascending part are sufficient.

Keywords

Main Subjects


  1. Abbott, M. B., & Ionescu, F. (1967). On the numerical computation of nearly horizontal flows. Journal of Hydraulic Research, 5(2), 97-117.
  2. Cupola, F., Tanda, M. G., & Zanini, A. (2015). Laboratory sandbox validation of pollutant source location methods. Stochastic Environmental Research and Risk Assessment, 29(1), 169-182.
  3. Fakoori Dekahi, B. (2017). Simulation of spatial and temporal variations in hydrodynamics and water salinity of Karun River (Molasani to Farsiat) with flow changes and loading management of pollution sources in the river. Master dissertation, Tarbiat Modares University, Iran. (In Persian).
  4. Ghane, A., Mazaheri, M., & Mohammad Vali Samani, J. (2016(. Location and release time identification of pollution point source in river networks based on the Backward Probability Method. Journal of Environmental Management, 180, 164-171.
  5. Jha, M. K., & Datta, B. (2011). Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers. Desalination and Water Treatment, 32(1-3), 79-85.
  6. Mahar, P. S., & Datta, B. (2001). Optimal identification of groundwater pollution sources and parameter estimation. Journal of Water Resources Planning and Management, 127 (1), 20-29.
  7. Mazaheri, M., Mohammad Vali Samani, J., & Mohammad Vali Samani, H. (2015). Mathematical Model for Pollution Source Identification in Rivers. Environmental Forensics, 16(4), 310-321.
  8. Nardo, A., Santonastaso, G. F., Battaglia, R., & Velotta, R. (2015). Smart identification system of surface water contamination by an innovative biosensor network. In: Proceeding of 5th international conference on Environmental Management, Engineering, Planning and Economics, 14 to 18 june, Mykonos island, Greece.
  9. Prakash, O., & Datta, B. (2014). Characterization of Groundwater Pollution Sources with Unknown Release Time History. Journal of Water Resource and Protection, 6, 337-350.
  10. Tong, Y., & Deng, Z. (2012). Moment-Based Method for Identification of Pollution Source in Rivers. Journal of Environmental Engineering, 141(10), 326-335.
  11. Yuan-hua, C., Peng, W., Ji-ping, J., & Liang, G. (2013). Contaminant point source identification of rivers chemical spills based on correlation coefficients optimization method. China Environmental Science, 31(11), 1802-1807.
  12. Zhang, S., & Xin, X. (2017). Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm. Applied Water Science, 7, 1955-1963.