A Conceptual Framework of a Surrogate-based Quality-Quantity Decision Support System (Q2DSS) for Water Resources Systems

Document Type: Research Paper

Authors

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

10.22055/jhs.2020.32755.1135

Abstract

The water crisis in different countries of the world has made the earth undergo tremendous changes compared to the past years. Therefore, it is very necessary to have intelligent systems that can help managers make correct and optimal decisions in various possible conditions. In recent years, the biggest challenge faced by water resource managers in the Karun Basin in Iran has been the decline in the quality of surface waters in the downstream areas of the basin. In this research, a surrogate-based model has been developed for predicting and controlling the quantity and quality of water in different parts of the basin. As a decision support system, this model can evaluate the quantity of water at different points in the basin and also predict its quality in various probable conditions. This model will also be used to extract optimal operating policies with the aim of satisfying quality constraints in different conditions. The model can help decision makers in the optimal management of the system and also greatly reduce the losses caused by quality issues in possible future situations.

Keywords


  1. Vaghefi, S. A., Mousavi, S. J., Abbaspour, K. C., Srinivasan, R., & Arnold, J. R. (2015). Integration of hydrologic and water allocation models in basin-scale water resources management considering crop pattern and climate change: Karkheh River Basin in Iran. Regional environmental change, 15(3), 475-484.
  2. Bakhsipoor, I. E., Ashrafi, S. M., & Adib, A. (2019). Water Quality Effects on the Optimal Water Resources Operation in Great Karun River Basin. Pertanika J. Sci. & Technol. 27 (4), pp: 1881–1900.
  3. Ashrafi, S. M. (2019). Investigating Pareto Front Extreme Policies Using Semi-distributed Simulation Model for Great Karun River Basin. Journal of Hydraulic Structures, 5(1), 75-88.
  4. Labadie, J. W. (2004). Optimal operation of multireservoir systems: state-of-the-art review. Journal of water resources planning and management, 130(2), 93-111.
  5. Ashrafi, S. M., & Dariane, A. B. (2017). Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems. Water Resources Management, 31(14), 4505-4520.
  6. Lk Velikanov, A. (1987). Problems of large-scale water resources systems development. In International symposium on water for the future, pp. 621-625.
  7. Rahaman, M. M., & Varis, O. (2005). Integrated water resources management: evolution, prospects and future challenges. Sustainability: science, practice and policy, 1(1), 15-21.
  8. Tundisi, J. G. (2008). Water resources in the future: problems and solutions. estudos avançados, 22(63), 7-16.
  9. Wang, K. W., Chang, L. C., & Chang, F. J. (2011). Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Advances in Water Resources, 34(10), 1343-1351.
  10. Cosgrove, W. J., & Loucks, D. P. (2015). Water management: Current and future challenges and research directions. Water Resources Research, 51(6), 4823-4839.
  11. Ashrafi, S. M., & Mahmoudi, M. (2019). Developing a semi-distributed decision support system for great Karun water resources system. Journal of Applied Research in Water and Wastewater, 6(1), 16-24.
  12. Fa'al, F., Ghafouri, H. R., & Ashrafi, S. M. (2020). Predicting Saltwater Intrusion into Coastal Aquifers Using Support Vector Regression Surrogate Models. Journal of Water and Wastewater, 31(2).
  13. Raad, D. N., Sinske, A. N., & Van Vuuren, J. H. (2010). Comparison of four reliability surrogate measures for water distribution systems design. Water Resources Research, 46(5).
  14. Tanyimboh, T. T., Tietavainen, M. T., & Saleh, S. (2011). Reliability assessment of water distribution systems with statistical entropy and other surrogate measures. Water Science and Technology: Water Supply, 11(4), 437-443.
  15. Herrera, M., Abraham, E., & Stoianov, I. (2015). Graph-theoretic surrogate measures for analysing the resilience of water distribution networks. Procedia Engineering, 119, 1241-1248.
  16. Gheisi, A., & Naser, G. (2015). Multistate reliability of water-distribution systems: comparison of surrogate measures. Journal of Water Resources Planning and Management, 141(10), 04015018.
  17. Ayati, A. H., Haghighi, A., & Lee, P. (2019). Statistical review of major standpoints in hydraulic transient-based leak detection. Journal of Hydraulic Structures, 5(1), 1-26.
  18. Wan, W., Guo, X., Lei, X., Jiang, Y., & Wang, H. (2018). A Novel Optimization Method for Multi-Reservoir Operation Policy Derivation in Complex Inter-Basin Water Transfer System. Water Resources Management, 32(1), 31-51.
  19. Ashrafi, S. M., Ashrafi, S. F., & Moazami, S. (2017). Developing Self-adaptive Melody Search Algorithm for Optimal Operation of Multi-reservoir Systems. Journal of Hydraulic Structures, 3(1), 35-48.
  20. Adib, A., Mirsalari, S. B., & Ashrafi, S. M. (2018). Prediction of meteorological and hydrological phenomena by different climatic scenarios in the Karkheh watershed (south west of Iran). Scientia Iranica (In Press). DOI: 10.24200/SCI.2018.50953.1934.
  21. Moazami, S., Abdollahipour, A., Zakeri Niri, M., & Ashrafi, S. M. (2016). Hydrological Assessment of Daily Satellite Precipitation Products over a Basin in Iran. Journal of Hydraulic Structures, 2(2), 35-45.
  22. Peng, H., Wang, Y., Zhang, W., Li, Y., Wu, K. B., & Zhu, Q. (2009, June). A coupled water quality-quantity model for water resource allocation. In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (pp. 1-5). IEEE.
  23. Zhang, W., Wang, Y., & Peng, H. (2009). An Integrated Water Quality-Quantity Method for Water Resource Management. In 2009 International Conference on Environmental Science and Information Application Technology (Vol. 2, pp. 178-181). IEEE.
  24. Bin, Z., & Zengchuan, D. (2004). Allocation model of regional water supply in different quality. Yangtze River, 35(2), 21-31.
  25. Abolpour, M. J., M. Karamouz (2005). Water allocation improvement in river basin using adaptive neural fuzzy reinfourcement learning approach. Applied Soft Computing 7(2007): 265-285.
  26. Xing, L., Y. Kan, et al. (2006). Allocation model of regional water resources in water supply in different quality. Journal of Heilongjiang Hydraulic Engineering College (2): 67-70.
  27. Karamouz M, Moridi A and Fayyazi HM (2008) Dealing with conflict over water quality and quantity allocation: a case study. Journal of Scientia Iranica 15(1): 34–49.
  28. Abbasnia, I. and Mosavi, J. (2010). A basin-scale quantity and quality allocation model of surface water resources. Proceedings of the 8th International Congress of Civil Engineering. University of Shiraz, Shiraz, Iran.
  29. Zhang, Z., & Johnson, B. E. (2016). Aquatic nutrient simulation modules (NSMs) developed for hydrologic and hydraulic models (No. ERDC/EL TR-16-1). US Army Engineer Research and Development Center Vicksburg United States.
  30. Azmi, M., & Heidarzadeh, N. (2013). Dynamic modelling of integrated water resources quality management. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 166, No. 7, pp. 357-366). Thomas Telford Ltd.
  31. Fontane D, Labadie J and Loftis B (1981). Optimal control of reservoir discharge quality through selective withdrawal. Water Resources Research 17(6): 1594–1604.
  32. Sasikumar K. and Mujumdar PP. (1998). Fuzzy optimization model for water quality management of a river system. Water Resources Planning and Management 124(2): 79–88.
  33. Azevedo, L. G. T. D., Gates, T. K., Fontane, D. G., Labadie, J. W., & Porto, R. L. (2000). Integration of water quantity and quality in strategic river basin planning. Journal of water resources planning and management, 126(2), 85-97.
  34. Erbe V and Schutze M (2005) An integrated modelling concepts for emission-based management of sewer system, wastewater treatment plant and river. Water Science and Technology 52(5): 95–103.
  35. Kerachian R and Karamouz M (2005) Waste load allocation model for seasonal river water quality management: application of sequential dynamic genetic algorithm. Journal of Scientia Iranica 12(2): 117–130.
  36. Schmitt, T. G., & Huber, W. C. (2006). The scope of integrated modelling: system boundaries, sub-systems, scales and disciplines. Water science and technology, 54(6-7), 405-413.
  37. Winz, I., Brierley, G., & Trowsdale, S. (2009). The use of system dynamics simulation in water resources management. Water resources management, 23(7), 1301-1323.
  38. Freni, G., Mannina, G., & Viviani, G. (2010). Urban water quality modelling: a parsimonious holistic approach for a complex real case study. Water Science and Technology, 61(2), 521-536.
  39. Saadatpour M, Afshar A, Edinger JE (2017). Meta-model assisted 2D hydrodynamic and thermal simulation model (CE-QUAL-W2) in deriving optimal reservoir operational strategy in selective withdrawal scheme. Water Resource Manage. 31(9):2729–2744.
  40. Rousta, B. A., & Araghinejad, S. (2015). Development of a multi criteria decision making tool for a water resources decision support system. Water Resources Management, 29(15), 5713-5727.
  41. Ashrafi, S. M., & Kourabbaslou, N. E. (2015). An Efficient Adaptive Strategy for Melody Search Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 6(3), 1-37.
  42. Bozorg-Haddad, O., Garousi-Nejad, I., & Loáiciga, H. A. (2017). Extended multi-objective firefly algorithm for hydropower energy generation. Journal of Hydroinformatics, 19(5), 734-751.
  43. Yang, Z., Yang, K., Wang, Y., Su, L., & Hu, H. (2019). The improved multi-criteria decision-making model for multi-objective operation in a complex reservoir system. Journal of Hydroinformatics, 21(5), 851-874.
  44. Soghrati, F., & Moeini, R. (2019). Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions. Journal of Hydroinformatics.
  45. Caloiero, T., Coscarelli, R., & Ferrari, E. (2019). Assessment of seasonal and annual rainfall trend in Calabria (southern Italy) with the ITA method. Journal of Hydroinformatics.
  46. Lerma, N., Paredes-Arquiola, J., Molina, J. L., & Andreu, J. (2014). Evolutionary network flow models for obtaining operation rules in multi-reservoir water systems. Journal of Hydroinformatics, 16(1), 33-49.
  47. Gordillo, G., Morales-Hernández, M., & García-Navarro, P. (2019). Finite volume model for the simulation of 1D unsteady river flow and water quality based on the WASP. Journal of Hydroinformatics.
  48. Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., ... & Ostfeld, A. (2014). Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling & Software, 62, 271-299.
  49. Aydin, N. Y., Zeckzer, D., Hagen, H., & Schmitt, T. (2015). A decision support system for the technical sustainability assessment of water distribution systems. Environmental Modelling & Software, 67, 31-42.
  50. Rani, D., & Moreira, M. M. (2010). Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water resources management, 24(6), 1107-1138.
  51. Falalakis, G., & Gemitzi, A. (2020). A simple method for water balance estimation based on the empirical method and remotely sensed evapotranspiration estimates. Journal of Hydroinformatics.
  52. Li, Y., Liang, Z., Hu, Y., Li, B., Xu, B., & Wang, D. (2019). A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy. Journal of Hydroinformatics.
  53. Lee, Y., Kim, S. K., & Ko, I. H. (2008). Multistage stochastic linear programming model for daily coordinated multi-reservoir operation. Journal of Hydroinformatics, 10(1), 23-41.
  54. Ehteram, M., Mousavi, S. F., Karami, H., Farzin, S., Singh, V. P., Chau, K. W., & El-Shafie, A. (2018). Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty. Journal of Hydroinformatics, 20(2), 332-355.