Skip to main content
SpringerLink
Log in

Hybrid ACO–ANN-Based Multi-objective Simulation–Optimization Model for Pollutant Load Control at Basin Scale

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

The systematic and integrative approach to optimum watershed management couples a watershed simulation model and an efficient optimization algorithm for evaluating great number of “what if” scenarios in the search domain. This study integrates a multi-objective Non-dominated Archiving Ant Colony Optimization (NA-ACO) algorithm as an optimization tool with Soil and Water Assessment Tool (SWAT) as the simulation module for optimum management of total suspended solids (TSS) loading to downstream water bodies. The resulting NA-ACO–SWAT model is computationally and experimentally expensive because of the large number of required function evaluations which demands repetitive execution of SWAT simulation model. To increase the computational efficiency of the watershed simulation model, the SWAT model is replaced by a trained artificial neural network (ANN) model to form a hybrid NA-ACO–SWAT–ANN model to efficiently develop the set of optimum non-dominated solutions for configuration and design of detention ponds in basin scale. The applicability of the proposed method is evaluated at Gharesou watershed in the northwest of Iran. The outcomes of the proposed approach is further analyzed and compared in terms of their quality of solutions and computational efficiencies. Results show that the proposed hybrid approach may reduce the computational time by 90 % while keeping the accuracy of the results in the same order.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Abbaspour, K. C., Schulin, R., & Genuchten, M. T. V. (2001). Estimating unsaturated soil hydraulic parameters using ant colony optimization. Advances in Water Resources, 24(8), 827–841.

    Article  Google Scholar 

  2. Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., & Srinivasan, R. (2007). Modelling hydrology and water quality in the pre‐alpine/alpine thur watershed using SWAT. Journal of Hydrology, 333(2–4), 413–430.

    Article  Google Scholar 

  3. Afshar, M. H., Afshar, A., Marino, M. A., & Darbandi, A. A. S. (2006). Hydrograph-based storm sewer design optimization by genetic algorithm. Canadian Journal of Civil Engineering, 33(3), 319–325.

    Article  Google Scholar 

  4. Afshar, M. H., & Marino, M. A. (2006). Application of an ant algorithm for layout optimization of tree networks. Engineering Optimization, 38(3), 353–369.

    Article  Google Scholar 

  5. Afshar, A., Sharifi, F., & Jalali, M. R. (2009). Non-dominated archiving multi-colony ant algorithm for multi-objective optimization: application to multi-purpose reservoir operation. Engineering Optimization, 41(4), 313–325.

    Article  Google Scholar 

  6. Afshar, A., & Kazemi, H. (2012). Multi objective calibration of large scaled water quality model using a hybrid particle swarm optimization and neural network algorithm. KSCE Journal of Civil Engineering, Environmental Engineering, 16(6), 913–918.

    Article  Google Scholar 

  7. Arabi, M., Govindaraju, R. S., Hantush, M. M., & Engel, B. A. (2006). Role of watershed subdivision on modeling the effectiveness of best management practices with SWAT. Journal of American Water Resources Association, 42(2), 513–528.

    Article  CAS  Google Scholar 

  8. Arnold, J. G., Srinisvan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment, part I: model development. Journal of American Water Resource Association, 34(1), 73–89.

    Article  CAS  Google Scholar 

  9. Back, A. D., & Trappenberg, T. P. (2001). Selecting inputs for modeling using normalized higher order statistics and independent component analysis. IEEE Transactions in Neural Networks, 12(3), 612–617.

    Article  CAS  Google Scholar 

  10. Baran, B., Schaerer M., (2003). A multi-objective ant colony system for vehicle routing problem with time windows. Twenty first IASTED International Conference on Applied Informatics, Insbruck, Austria, February 10–13, pp. 97–102.

  11. Barton, R. R., (1998). SIMULATION METAMODELS. Proceedings of the 1998 Winter Simulation Conference, Medeiros, D.J., Watson, E.F., Carson, J.S., and Manivannan, M.S. as editors. pp. 167–174.

  12. Bozorg Haddad, O., Afshar, A., & Marino, M. A. (2006). Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resources Management, 20(5), 661–680.

    Article  Google Scholar 

  13. Bracmort, K. S., Arabi, M., Frankenberger, J. R., Engel, B. A., & Arnold, J. G. (2006). Modeling long‐term water quality impact of structural BMPs. Transactions American Society of Agricultural and Biological Engineers, 49(2), 367–374.

    CAS  Google Scholar 

  14. Broad, D., Dandy, G. C., & Maier, H. R. (2005). Water distribution systems optimization using metamodels. Water Resources Planning and Management, ASCE, 131(3), 172–180.

    Article  Google Scholar 

  15. Chu, T. W., & Shirmohammadi, A. (2004). Evaluation of the SWAT model’s hydrology component in the piedmont physiographic region of Maryland. Transaction of ASAE, 47(4), 1057–1073.

    Article  Google Scholar 

  16. Deb, K. (2001). Multi objective optimization using evolutionary algorithms. New York: Wiley.

    Google Scholar 

  17. Demirel, M. C., Venancio, A., & Kahya, E. (2009). Flow forecast by SWAT model and ANN in pracana basin, Portugal. Advances in Engineering Software, 40(7), 467–473.

    Article  Google Scholar 

  18. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). The ant system: optimization by a colony of cooperating ants. IEEE Transaction on System Management Cybernetics, 26(1), 29–41.

    Article  CAS  Google Scholar 

  19. Doerner, K., Gutjahr, W. J., Hartl, R. F., Strauss, C., & Stummer, C. (2004). Pareto ant colony optimization: a meta-heuristic approach to multi-objective portfolio selection. Annals of Operations Research, 131(1–4), 79–99.

    Article  Google Scholar 

  20. Elliot, A. H. (1998). Model for preliminary catchment scale planning of urban storm water quality controls. Journal of Environmental Management, 52(3), 273–288.

    Article  Google Scholar 

  21. Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G. (2007). The soil and water assessment tool: historical development, applications and future research directions. Invited Review Series, American Society of Agricultural and Biological Engineers, 50(4), 364–381.

    Google Scholar 

  22. Harrell, L. J., & Ranjithan, S. R. (2003). Detention pond design and land Use planning for watershed management. Journal of Water Resources Planning and Management, 129(2), 98–106.

    Article  Google Scholar 

  23. Jalali, M. R., Afshar, A., & Marino, M. A. (2007). Multi-colony ant algorithm for continuous multi-reservoir operation optimization problem. Water Resources Management, 21(9), 1429–1447.

    Article  Google Scholar 

  24. Johnson, V. M., & Rogers, L. L. (2000). Accuracy of neural network approximators in simulation-optimization. Water Resources Planning and Management, ASCE, 126(2), 48–56.

    Article  Google Scholar 

  25. Karamouz, M., Taheriyoun, M., Baghvand, A., Tavakolifar, H., & Emami, F. (2010). Optimization of watershed control strategies for reservoir eutrophication management. Journal of Irrigation and Drainage Engineering, 136(12), 847–861.

    Article  Google Scholar 

  26. Kaini, P., Artita, K., & Nicklow, J. W., (2009). Generating different scenarios of BMP designs in a watershed scale by combining NSGA-II with SWAT. World Environmental and Water Resources Congress 2009: Great Rivers, ASCE.,1-9.

  27. Kaur, R., Singh, O., Srinivasan, R., Das, S. N., & Mishra, K. (2004). Comparison of a subjective and a physical approach for identification of priority areas for soil and water management in a watershed: a case study of Nagwan watershed in Hazaribagh District of Jharkhand, India. Environmental Modelling and Assessment, 9(2), 115–127.

    Article  Google Scholar 

  28. Madadgar, S., & Afshar, A. (2009). An improved continuous Ant algorithm for optimization of water resources problems. Water Resources Management, 23(10), 2119–2139.

    Article  Google Scholar 

  29. Maier, H. R., Simpson, A. R., Zecchin, A. C., Foong, W. K., Phang, K. Y., Seah, H. Y., & Tan, C. L. (2003). Ant colony optimization for the design of water distribution systems. Journal of Water Resources and Planning Management, 129(3), 200–209.

    Article  Google Scholar 

  30. Mousavi, S. J., Ponnambalam, K., & Karray, F. (2007). Inferring operating rules for reservoir operation using fuzzy regression and ANFIS. Fuzzy Sets Systems, 158(10), 1064–1082.

    Article  Google Scholar 

  31. Ostadrahimi, L., Marino, M. A., & Afshar, A. (2012). Multi-reservoir operation rules: multi-swarm PSO-based optimization approach. Water Resources Management, 26(2), 407–427.

    Article  Google Scholar 

  32. Qi, H., & Altinakar, M. S. (2011). A conceptual framework of agricultural land use planning with BMP for integrated watershed management. Journal of Environmental Management, 92(1), 149–55.

    Article  Google Scholar 

  33. Qi, H., & Altinakar, M. S. (2011). Vegetation buffer strips design using an optimization approach for Non-point source pollutant control, of an agricultural watershed. Water Resources Management, 25(2), 565–578.

    Article  Google Scholar 

  34. Ranjithan, S., Eheart, J. W., & Garrett, J. H. (1993). Neural network based screening for groundwater reclamation uncertainty. Water Resources Research, 29(3), 563–574.

    Article  Google Scholar 

  35. Rogers, L. L., & Dowla, F. U. (1994). Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resources Research, 30(2), 457–481.

    Article  CAS  Google Scholar 

  36. Rostamian, R., Jaleh, A., Afyuni, M., Mousavi, S. F., Heidarpour, M., Jalalian, A., & Abbaspour, K. C. (2008). Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences-Journal, 53(5), 977–988.

    Article  Google Scholar 

  37. Sadeghi, S., (2010). Identifying and prioritizing causing erosion areas in the Gharesou Watershed. M.S. Dissertation, Department of civil engineering of Islamic Azad University, Science and Research Branch, Iran, Tehran.

  38. Santhi, C., Arnold, J. G., Williams, J. R., Dugas, W. A., Srinivasn, R., & Hauck, L. M. (2001). Validation of the SWAT model on a large river basin with point and nonpoint sources. Journal of American Water Resources Association, 37(5), 1169–1188.

    Article  CAS  Google Scholar 

  39. Santhi, C., Srinivasan, R., Arnold, J. G., & Williams, J. R. (2006). A modeling approach to evaluate the impacts of water quality management plans implemented in a watershed in Texas. Environmental Modelling and Software, 21(8), 1141–1157.

    Article  Google Scholar 

  40. Shepherd, B., Harper, D., & Millington, A. (1999). Modeling catchment-scale nutrient transport to watercourses in the UK. Hydrobiologia, 395–396, 227–238.

    Article  Google Scholar 

  41. Shourian, M., Mousavi, S. J., Menhaj, M., & Jabbari, E. (2008). Neural network‐based simulation optimization model for optimal water allocation planning at basin scale. Journal of Hydroinformatics, 10(4), 331–343.

    Article  Google Scholar 

  42. Sindelar, R., & Babuska, R. (2004). Input selection for nonlinear regression models. IEEE Transactions on Fuzzy Systems, 12(5), 688–696.

    Article  Google Scholar 

  43. Emami Skardi, M. J., (2011). Design of detention ponds for watersheds, using ant colony algorithm (AC) and SWAT model; a cooperative game theory approach, MS Dissertation, Afshar, A., as Advisor, Iran University of Science and Technology.

  44. Skardi, M. J., Afshar, A., & Sandoval Solis, S. (2013). Simulation-optimization model for Non-point source pollution management in watersheds: application of cooperative game theory. KSCE Journal of Civil Engineering, 17(6), 1232–1240.

    Article  Google Scholar 

  45. Smith, J., & Eli, R. N. (1995). Neural network models for rainfall runoff Process. Water Resources Planning and Management, ASCE, 121(6), 499–508.

    Article  Google Scholar 

  46. Spruill, C. A., Workman, S. R., & Taraba, J. L. (2000). Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Transaction of ASAE, 43(6), 1431–1439.

    Article  Google Scholar 

  47. Sureerattanan, S., & Phein, H. N., (1997). Back propagation networks for daily stream flow forecasting. Water Resources Research, C/195, 1–7.

  48. Tolson, B. A., & Shoemaker, C. A., (2004). Watershed modeling of the Cannonsville Basin using SWAT2000: model development, calibration and validation for the prediction of flow, sediment and phosphorus transport to the Cannonsville Reservoir. Technical Report, School of Civil and Environmental Engineering, Cornell University., Ithaca, N. Y.

  49. Vaché, K. B., Eilers, J. M., & Santelman, M. V. (2002). Water quality modeling of alternative agricultural scenarios in the U.S. Corn Belt. Journal of American Water Resources Association, 38(2), 773–787.

    Article  Google Scholar 

  50. Wagner, B. J. (1995). Sampling design methods for groundwater modeling under uncertainty. Water Resources Research, 31(10), 2581–2591.

    Article  Google Scholar 

  51. Wang, G. G., & Shan, S. (2006). Review of meta‐modeling techniques in support of engineering design optimization. Journal of Mechanical Design, 76(5), 34–56.

    Google Scholar 

  52. Yan, S., & Minsker, B., (2006). Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resources Research, 42(5), 1–14. W05407.

  53. Zecchin, A. C., Maier, H. R., Simpson, A. R., Roberts, A. J., Berrisford, M. J., & Leonard, M., 2003. Max-min ant system applied to water distribution system optimization. International Congress on Modeling Simulation (MODSIM), Vol. 2, Townsville, Australia, pp. 795–800.

  54. Zhang, X., Srinivasan, R., Debele, B., & Hao, F. (2008). Runoff simulation of the headwaters of the yellow river using the swat model with three snowmelt algorithms. Journal of American Water Resources Association, 44(1), 48–61.

    Article  Google Scholar 

  55. Zhang, X., Srinivasan, R., & Van Liew, M. (2009). Approximating SWAT model using artificial neural network and support vector machine. Journal of American Water Resources Association, 45(2), 460–474.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Mohammad J. Emami Skardi

  2. Department of Land, Air and Water Resources, University of California, Davis, CA, USA

    Abbas Afshar & Samuel Sandoval Solis

  3. School of Environmental Engineering, University of Environment, Karaj, Iran

    Motahareh Saadatpour

Authors
  1. Mohammad J. Emami Skardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbas Afshar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Motahareh Saadatpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samuel Sandoval Solis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad J. Emami Skardi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Emami Skardi, M.J., Afshar, A., Saadatpour, M. et al. Hybrid ACO–ANN-Based Multi-objective Simulation–Optimization Model for Pollutant Load Control at Basin Scale. Environ Model Assess 20, 29–39 (2015). https://doi.org/10.1007/s10666-014-9413-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-014-9413-7

Keywords

Search

Navigation