The Comparison of Inverse approaches Simulation-Optimization and Surrogate Transport Model for Pollution Source Characteristics Identification in Aquifer-River Integrated Systems

Document Type : Research Paper


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

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

3 Retired Professor, Civil Engineering Department, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Iran (Visiting Professor at Tarbiat Modares University).

4 Associate Professor, Engineering and Water Management Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran


The identification of potential pollution sources and their continuous monitoring is one of the most important measures in the quality management of groundwater and surface water resources. Since the relation between these two systems and the injected pollution pattern at the source is not easily discernible, inverse methods are recommended. In this paper, the inverse solution of the ADE equation is conducted using the simulation-optimization approach to identify the characteristics of a pollution source that is released in a confined aquifer and reaches a river, then moves along the stream to a monitoring cross-section where it is detected. The proposed case studies were not investigated before.
The inverse method combines the forward model and an optimization algorithm. To speed up the computation, the transfer function theory is applied to create a surrogate transport forward model. The two approaches are compared in terms of accuracy and speed of solution for two hypothetical cases (The second example, considering the geometric dimensions of the Karun River in Iran). The result show transfer function methodology used to create a surrogate transport model is convenient, very fast compared to other existing approaches, and more accurate in the reconstruction of source characteristics even in presence of noise on observations. Moreover, each application of the transfer function to surrogate the transport process requires only 0.56 percent of the computation time of the complete simulation model. So due to its effect on significantly increasing the reverse resolution speed, it can be used for real scenarios of pollutant transport problems that generally face time constraints.


Main Subjects

  1. Anderson, M. P., Woessner, W. W., & Hunt, R. J. (1992). Applied groundwater modeling: Simulation of flow and advective transport. Journal of Hydrology, 140, 393–395.
  2. Ayaz, M., Srivastava, R., & Jain, A. (2014). Groundwater pollution source identification using linked ANN-optimization model, in: EGU General Assembly Conference Abstracts, 27 April, Vienna, Austria, id. 830.
  3. Ayvaz, M. T. (2010). A linked simulation–optimization model for solving the unknown groundwater pollution source identification problems. Journal of Contaminant Hydrology, 117(1-4), 46–59.
  4. Barron, A. R., & Xiao, X. (1991). Discussion: multivariate adaptive regression splines. Annals of Statistics, 19(1), 67–82.
  5. Bear, J., & Verruijt, A. (1987). Modeling groundwater flow and pollution. Dordrecht, Netherlands, Reidel Publ.
  6. Behzadian, K., Kapelan, Z., Savic, D., & Ardeshir, A. (2009). Environmental modelling & software stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environmental Modeling & Software, 24(4), 530–541.
  7. Boano, F., Revelli, R., & Ridolfi, L. (2005). Source identification in river pollution problems : A geostatistical approach. Water Resources Research, 41(7), 1-13.
  8. Borah, T., & Bhattacharjya, R. K. (2015). Development of Unknown Pollution Source Identification Models Using GMS ANN–Based Simulation Optimization Methodology. Journal of Hazardous, Toxic, and Radioactive Waste, 19(3), 4014034.
  9. Butera, I., Tanda, M. G., & Zanini, A. (2013). Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach. Stochastic Environmental Research and Risk Assessment, 27(5), 1269–1280.
  10. Butera, I., Tanda, M. G., & Zanini, A. (2006). Use of numerical modelling to identify the transfer function and application to the geostatistical procedure in the solution of inverse problems in groundwater. Journal of Inverse and Ill-posed Problems, 14(6), 547–572.
  11. Byrd, R. H., Hribar, M. E., & Nocedal, J. (1999). An interior point algorithm for large-scale nonlinear programming. Aociety for Industrial and Applied Mathematics Journal on Optimization, 9(4), 877–900.
  12. Fen, C., Chan, C., & Cheng, H. (2009). Assessing a response surface-based optimization approach for soil vapor extraction system design. Journal of Water Resources Planning and Management, 135(3), 198.
  13. Guo, J. Y., Lu, W. X., Yang, Q. C., & Miao, T. S. (2019). The application of 0–1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source. Journal of Contaminant Hydrology, 220, 18-25.
  14. Hazrati-yadkoori, S., & Datta, B. (2017a). Self-organizing map based surrogate models for contaminant source identification under parameter uncertainty. International Journal of GEOMATE, 13(36), 10–18.
  15. Hazrati-yadkoori, S., & Datta, B. (2017b). Adaptive surrogate model based optimization (ASMBO) for unknown groundwater contaminant source characterizations using self-organizing maps. Journal of Water Resource and Protection, 9(2), 193–214.
  16. Jamshidi, A., Samani, J. M. V., Samani, H. M. V., Zanini, A., Tanda, M. G., & Mazaheri, M. (2020). Solving inverse problems of unknown contaminant source in groundwater-river integrated systems using a surrogate transport model based 17. optimization. Water, 12(9), 2415.
  17. Mekonnen, M. M., & Gerbens-Leenes, W. (2020). The water footprint of global food production. Water12(10), 2696.

    1. Mekonnen, M.M., & Hoekstra, A.Y. (2012). A global assessment of the water footprint of farm animal products. Ecosystems15(3), 401-415.
    2. Mohammadi, A., Yousefi, H., Noorollahi, Y., & Sadatinejad, S. (2017). Choosing the best province in potato production using water footprint assessment. Iranian journal of Ecohydrology, 4(2), 523-532. (in Persian)
    3. Nouri, H., Stokvis, B., Galindo, A., Blatchford, M., & Hoekstra, A. Y. (2019). Water scarcity alleviation through water footprint reduction in agriculture: the effect of soil mulching and drip irrigation. Science of the total environment653, 241-252.
    4. Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science360(6392), 987-992.
    5. Soltani, A., Alimagham, S. M., Nehbandani, A., Torabi, B., Zeinali, E., Zand, E., ... & van Ittersum, M. K. (2020). Modeling plant production at country level as affected by availability and productivity of land and water. Agricultural Systems183, 102859.
    6. Stocker, T. F., Qin, D., Plattner, G. K., Alexander, L. V., Allen, S. K., Bindoff, N. L., ... & Xie, S. P. (2013). Technical summary. In Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change(pp. 33-115). Cambridge University Press.
    7. Weidema, B. P., Bauer, C., Hischier, R., Mutel, C. L., Nemecek, T., Reinhard, J., ... & Wernet, G. (2013). Data quality guidelines for the ecoinvent database version 3: Overview and methodology (final).
    8. West, T. O., & Marland, G. (2002). A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: comparing tillage practices in the United States. Agriculture, Ecosystems & Environment91(1-3), 217-232.
    9. Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., ... & Murray, C. J. (2019). Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet393(10170), 447-492.
    10. WWAP (United Nations World Water Assessment Programme). (2014). The United Nations World Water Development Report 2014: Water and Energy. Paris, UNESCO.
    11. Xie, H., Wang, L., & Chen, X. (2008). Improvement and application of ecological footprint model.
    12. Xiong, X., Zhang, L., Hao, Y., Zhang, P., Chang, Y., & Liu, G. (2020). Urban dietary changes and linked carbon footprint in China: a case study of Beijing. Journal of environmental management255, 109877.
    13. Yang, S.H., (1996). The research of City Trees effects of carbon and oxygen balance. City Environment & Ecology, 9(001), 37-39. (In Chinese)
    14. Yousefi, H., Mohammadi, A., Noorollahi, Y., & Sadatinejad, S. (2018). Water footprint evaluation of Tehran’s crops and garden crops. Journal of Water and Soil Conservation, 24(6), 67-85. (In Persian)
    15. Yuan, Q., Song, G., Fullana-i-Palmer, P., Wang, Y., Semakula, H. M., Mekonnen, M. M., & Zhang, S. (2017). Water footprint of feed required by farmed fish in China based on a Monte Carlo-supported von Bertalanffy growth model: A policy implication. Journal of Cleaner Production153, 41-50.