Recovering the salinity intensity of distributed sources in the river using inverse simulation-optimization approach

Document Type : Research Article


1 Department of Water Structures Engineering/Faculty of Agriculture,/Tarbiat Modares University/ tehran/iran

2 Department of Water Structures Engineering/Faculty of Agriculture,/Tarbiat Modares University/tehran/iran

3 Department of Water Engineering/ Faculty of Civil Engineering/Shahid Chamran University/ahvaz/iran


In recent years, the issue of identifying the polluting sources in the rivers has been one of the most important topics in scientific research in the field of water. In the main research, the pollutant sources have been considered as the point sources, and in order to recover pollutant concentration, it is necessary to have an observation point for each source. In this study, the places where groundwater enters to river are considered as distributed sources with known locations and length and the goal is to recover the intensity of sources, using only one observation point. The sources which considered are distributed sources with constant loading and significant distance from each other. The existence of distance among sources prevents the complete mixing of concentration at the observation point. This matter and also the constant intensity of loading, makes it possible to recover several distributed sources using only one observation point. For this purpose, the inverse solution of the advection-dispersion equation is done using the simulation-optimization approach. To design the backward model, MIKE11, linked with a genetic algorithm in MATLAB. Considering one observation point for recovering the intensity of several distributed sources is the advantage of the present study. The model was verified by using hypothetical examples, 40km section of Karun River, and by applying 5 and 15 percent noise to the observation data. The results demonstrate that the backward model can recover the intensity of several sources not only with one observation point but also with data from the concentration versus time curve at the observation point. The accuracy of the model in recovering resource intensity, according to statistical indicators, is more than 99%.


Main Subjects

[1] M. Mazaheri, Mathematical Model for Identification of Pollution Sources in the Rivers: Reconstruction of Location and Release History of the Sources, Ph.D. Thesis in Water Structures Engineering, Department of Water Structures Engineering, Tarbiat Modares University, Iran, (2011) (In Persian).
[2] W. P. Cheng, Y. Jia, Identification of contaminant point source in surface waters based on backward location probability density function method, Advances in Water Resources, 33(4) (2010) 397–410.
[3] Q. a. Dang, M. Ehrhardt, G. L. Tran, D. Le, Mathematical Modeling and Numerical Algorithms for Simulation of Oil Pollution, Environmental Modeling and Assessment, 17(3) (2011) 275–288.
[4] C. Yuan-hua, W. Peng, J. Ji-ping, G. Liang, Contaminant point source identification of rivers chemical spills based on correlation coefficients optimization method, China Environmental Science, 31(11) (2013) 1802-1807.
[5] H. Yang, D. Shao, B. Liu, J. Huang, X. Ye, Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo, Stochastic Environmental Research and Risk Assessment, 30 (2) (2015) 507-522.
[6] A. Ghane, M. Mazaheri, J.M.V. Samani, Location and release time identification of pollution point source in river networks based on the Backward Probability Method, Journal of environmental management, 180 (2016) 164-171.
[7] Y. J. Lee, C. Park, M.L. Lee, Identification of a Contaminant Source Location in a River System Using Random Forest Models, Water Journal, 10(4) (2018) 391.
[8] M. BaratiMoghaddam, M. Mazaheri, J.M.V. Samani, F. Boano, An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks, Journal of Stochastic Environmental Research and Risk Assessment, (2022) 1-28.
[9] Y. Tong, Z. Deng, Moment-Based Method for Identification of Pollution Source in Rivers, Journal of Environmental Engineering, 141(10) (2012) 04015026.
[10] Z. Wang, J. Liu, Identification of the pollution source from one-dimensional parabolic equation models, Applied Mathematics and Computation, 219(8) (2012) 3403-3413.
[11] T. S. Li, S. M. Wong, Development of an Efficient and Accurate Global Space-Time Radial Basis Collocation Model for Estimation of River Pollution Source, International Journal of Engineering and Technology, 6(2) (2014)136–140.
[12] M. Mazaheri, J.M.V. Samani, H.M.V. Samani, Mathematical model for pollution source identification in rivers, Environmental Forensics, 16(4) (2015) 310-321.
[13] A. Hamdi, Detection and identification of multiple unknown time-dependent point sources occurring in 1D evolution transport equations. Inverse Problems in Science and Engineering, 25 (2017), 532-554.
[14] N. Mashhadgarme, M. Mazaheri, J.M.V. Samani, An Analytical solution to two-dimensional unsteady pollutant transport equation with arbitrary initial condition and source term in the open channels, Journal of the Earth and Space Physics, 47(1) (2021) 77-90, (In Persian).
[15] M. Amirabdollahian, B. Datta, Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field, International Journal of Environmental Science and Development, 6(7) (2015) 512-518.
[16] M.K. Jha, B. Datta, Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers, Desalination and Water Treatment, 32 (2011)79-85.
[17] I. T. Telci, M.M. Aral, Water Quality Exposure and Health, Springer, NewYork, 2011.
[18] A. Di Nardo, G. Santonastaso, R. Battaglia, D. Musmarra, F. Tuccinardi, F. Castaldo, B. Della Ventura, M. Iervolino, R. Velotta, Smart identification system of surface water contamination by an innovative biosensor network, in: Proceedings of Conference on Environmental Management, Engineering, Planning and Economics (CEMEPE) and to the SECOTOX Conference, 2015.
[19] S. p. Zhang, X. k. Xin, Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm, Applied Water Science, 7(4) (2017) 1955-1963.
[20] H. K. Esfahani, B. Datta, Fractal Singularity–Based Multiobjective Monitoring Networks for Reactive Species Contaminant Source Characterization, Journal of Water Resources Planning and Management, 144 (2018), 04018021.
[21] W. Lu, H. Wang, J. Li, Parallel heuristic search strategy based on a Bayesian approach for simultaneous recognition of contaminant sources and aquifer parameters at DNAPL-contaminated sites, Environmental Science and Pollution Research, (2020), 1-15.
[22] A. Jamshidi, J. M. V. Samani, H. M.V. Samani, A. Zanini, M.G. Tanda, M. Mazaheri, Solving inverse problems of unknown contaminant source in groundwater-river integrated systems using a surrogate transport model based optimization, Water, 12(9) (2020), 2415.
[23] A. Jamshidi, J. M. V. Samani, H. M.V. Samani, M. Mazaheri. The Comparison of Inverse approaches Simulation-Optimization and Surrogate Transport Model for Pollution Source Characteristics Identification in Aquifer-River Integrated Systems, Water and Irrigation Management, 11(2) (2021) 325-343, (In Persian).
[24] E. Essouayed, E. Verardo, A. Pryet, R. Chassagne, O. Atteia, An iterative strategy for contaminant source localisation using GLMA optimization and Data Worth on two synthetic 2D Aquifers, Journal of contaminant hydrology, 228 (2020), 103554.
[25] B. Fakoori Dekahi, 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 in Water Structures Engineering, Department of Water Structures Engineering, Tarbiat Modares University, Iran, (2017) (In Persian).
[26] P.S. Mahar, B. Datta, Optimal identification of groundwater pollution sources and parameter estimation, Journal of Water Resources Planning and Management, 127 (1) (2001) 20–29.