Abstract
The aim of this study is to improve the water quality of rivers while satisfying the interests of pollution sources and environmental protection agencies (EPA). For this purpose, a stochastic integrated simulation–optimization approach is developed for waste load allocation (WLA) in a river system. The water quality simulation model (QULA2Kw) is coupled with an evolutionary optimization model (multi-objective imperialist competition algorithm (MOICA)) to minimize wastewater treatment costs and biochemical oxygen demand (BOD) violations of the standard level. The applicability of the approach is demonstrated by the case study of the Dez River in Iran. The stochastic model (ARIMA) is used to forecast the headwater from 2022 to 2025. The influence of the uncertainty of the stochastic parameters (headwater, oxidation rate, point source inflow, abstraction, and point source concentration) is evaluated by the Generalized Likelihood Uncertainty Estimation (GLUE) model. The results showed that the point source inflow uncertainty is higher than other parameters. The results of optimal WLA under the uncertainties showed that the dissolved oxygen (DO) uncertainty bound was narrower than the BOD. The solutions in Pareto fronts showed the contradiction between polluters and environmentalists' interests, and according to the waste load criterion, using this methodology not only improved the river water quality but also there were least violations of standards along the river.
Similar content being viewed by others
Availability of Data, Material, and Code
Data and codes will be available upon request of the journal to them.
References
Afshar A, Emami Skardi MJ, Masoumi F (2015) Optimizing water supply and hydropower reservoir operation rule curves: an imperialist competitive algorithm approach. Eng Optim 47(9):1208–1225. https://doi.org/10.1080/0305215X.2014.958732
Aghasian K, Moridi A, Mirbagheri A, Abbaspour M (2019) A conflict resolution method for waste load reallocation in river systems. Int J Environ Sci Technol 16:79–88. https://doi.org/10.1007/s13762-018-1993-3
Atashpaz-Gargari E, Lucas C (2007, September) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congr Evol Comput 4661–4667. Ieee. https://doi.org/10.1109/CEC.2007.4425083
Azari A, Hamzeh S, Naderi S (2018) Multi-objective optimization of the reservoir system operation by using the hedging policy. Water Resour Manag 32:2061–2078. https://doi.org/10.1007/s11269-018-1917-5
Babamiri O, Marofi S (2021) A multi-objective simulation–optimization approach for water resource planning of reservoir–river systems based on a coupled quantity–quality model. Environ Earth Sci 80(11):389. https://doi.org/10.1007/s12665-021-09681-9
Babamiri O, Azari A, Marofi S (2022) An integrated fuzzy optimization and simulation method for optimal quality-quantity operation of a reservoir-river system. Water Supply 22(4):4207–4229. https://doi.org/10.2166/ws.2022.045
Babamiri O, Vanaei A, Guo X, Wu P, Richter A, Ng KTW (2021) Numerical simulation of water quality and self-purification in a mountainous river using QUAL2KW. J Environ Inform 37(1). https://doi.org/10.3808/jei.202000435
Beven K, Binley A (2014) GLUE: twenty years on. Hydrol Process 28(24):5897–5918. https://doi.org/10.1002/hyp.10082
Beven K, Smith P, Freer J (2007) Comment on “Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology” by Pietro Mantovan and Ezio Todini. J Hydrol 338(3–4):315–318. https://doi.org/10.1016/j.jhydrol.2007.02.023
Blasone RS, Vrugt JA, Madsen H, Rosbjerg D, Robinson BA, Zyvoloski GA (2008) Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Adv Water Resour 31(4):630–648. https://doi.org/10.1016/j.advwatres.2007.12.003
Box GEP, Jenkins GM (1976) Series analysis forecasting and control, 1st edn. Holden-Day, San Francisco, pp. 575. ISBN-10: 0816211043
Chapra SC, Pelletier GJ, Tao H (2008) QUAL2K: a modeling framework for simulating river and stream water quality, version 2.11: Documentation and user’s manual. Civil Environ Eng Dept, Tufts University, Medford, MA, 109. https://doi.org/10.1016/j.envsoft.2005.07.002
Chen L, Shen Z, Yang X, Liao Q, Shaw LY (2014) An interval-deviation approach for hydrology and water quality model evaluation within an uncertainty framework. J Hydrol 509:207–214. https://doi.org/10.1016/j.jhydrol.2013.11.043
Choi HT, Beven K (2007) Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework. J Hydrol 332(3–4):316–336. https://doi.org/10.1016/j.jhydrol.2006.07.012
Dai C, Qin XS, Chen Y, Guo HC (2018) Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach. J Hydrol 561:322–334. https://doi.org/10.1016/j.jhydrol.2018.04.012
DaSilva TD, Albuquerque Alves CDM (2016) WEAP and QUAL2 K model integration for water quality evaluations as a result of urban expansion scenarios in the Federal District of Brazil. World Environmental and Water Resources Congress 2016, pp 330–338. https://doi.org/10.1061/9780784479889.035
Ebrahimi S, Khorram M (2021) Variability effect of hydrological regime on river quality pattern and its uncertainties: case study of Zarjoob River in Iran. J Hydroinf 23(5):1146–1164. https://doi.org/10.2166/hydro.2021.027
Enayatifar R, Yousefi M, Abdullah AH, Darus AN (2013) MOICA: A novel multi-objective approach based on imperialist competitive algorithm. Appl Math Comput 219(17):8829–8841. https://doi.org/10.1016/j.amc.2013.03.099
Estalaki SM, Abed-Elmdoust A, Kerachian R (2015) Developing environmental penalty functions for river water quality management: Application of evolutionary game theory. Environ Earth Sci 73:4201–4213. https://doi.org/10.1007/s12665-014-3706-7
Feizi Ashtiani E, Niksokhan MH, Ardestani M (2015) Multi-objectiveWaste LoadAllocation in RiverSystembyMOPSOAlgorithm. Int J Environ Res 9(1):69–76. https://doi.org/10.22059/ijer.2015.875
Georgiou PE, Papamichail DM, Vougioukas SG (2006) Optimal irrigation reservoir operation and simultaneous multi-crop cultivation area selection using simulated annealing. Irrig Drain: J Int Comm Irrig Drain 55(2):129–144. https://doi.org/10.1002/ird.229
Gikas GD (2014) Water quality of drainage canals and assessment of nutrient loads using QUAL2Kw. Environ Process 1:369–385. https://doi.org/10.1007/s40710-014-0027-5
Gong Y, Shen Z, Hong Q, Liu R, Liao Q (2011) Parameter uncertainty analysis in watershed total phosphorus modeling using the GLUE methodology. Agr Ecosyst Environ 142(3–4):246–255. https://doi.org/10.1016/j.agee.2011.05.015
Haddad OB, Afshar A, Mariño MA (2006) Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour Manag 20:661–680. https://doi.org/10.1007/s11269-005-9001-3
Hosseini-Moghari SM, Morovati R, Moghadas M, Araghinejad S (2015) Optimum operation of reservoir using two evolutionary algorithms: imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water Resour Manag 29:3749–3769. https://doi.org/10.1007/s11269-015-1027-6
Hu P, Chen N, Li Y, Xie Q (2018) Efficiency evaluation of water consumption in a Chinese province-level region based on data envelopment analysis. Water 10(6):793. https://doi.org/10.3390/w10060793
Jalili AA, Najarchi M, Shabanlou S, Jafarinia R (2022) Multi-objective optimization of water resources in real time based on integration of NSGA-II and support vector machines. Environ Sci Pollut Res 1–12. https://doi.org/10.1007/s11356-022-22723-4
Juwana I, Rahardyan NA, Permadi DA, Sutadian AD (2022) Uncertainty and sensitivity analysis of the effective implementation of water quality improvement programs for Citarum River, West Java, Indonesia. Water 14(24):4077. https://doi.org/10.3390/w14244077
Khuzestan Water and Power Authority (KWPA) (2001) An assessment of pollutants in Dez River. A Report Prepared by the Water Quality Assessment Section. KWPA, Ministry of Power, Ahwaz, Iran
Kumar DN, Reddy MJ (2006) Ant colony optimization for multi-purpose reservoir operation. Water Resour Manag 20:879–898. https://doi.org/10.1007/s11269-005-9012-0
Liu D, Guo S, Shao Q, Jiang Y, Chen X (2014) Optimal allocation of water quantity and waste load in the Northwest Pearl River Delta, China. Stoch Environ Res Risk Assess 28:1525–1542. https://doi.org/10.1007/s00477-013-0829-4
Mahjouri N, Bizhani-Manzar M (2013) Waste load allocation in rivers using fallback bargaining. Water Resour Manag 27:2125–2136. https://doi.org/10.1007/s11269-013-0279-2
Mannina G (2011) Uncertainty assessment of a water-quality model for ephemeral rivers using GLUE analysis. J Environ Eng 137(3):177–186. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000312
Mantovan P, Todini E (2006) Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology. J Hydrol 330(1–2):368–381. https://doi.org/10.1016/j.jhydrol.2006.04.046
Meng C, Wang X, Li Y (2017) An optimization model for waste load allocation under water carrying capacity improvement management, a case study of the Yitong River. Northeast China Water 9(8):573. https://doi.org/10.3390/w9080573
Moridi A (2019) A bankruptcy method for pollution load reallocation in river systems. J Hydroinf 21(1):45–55. https://doi.org/10.2166/hydro.2018.156
Moshizi ZGN, Bazrafshan O, Etedali HR, Esmaeilpour Y, Collins B (2023) Application of inclusive multiple model for the prediction of saffron water footprint. Agric Water Manag 277:108125. https://doi.org/10.1016/j.agwat.2022.108125
Muronda MT, Marofi S, Nozari H, Babamiri O (2021) Uncertainty analysis of reservoir operation based on stochastic optimization approach using the generalized likelihood uncertainty estimation method. Water Resour Manag 35(10):3179–3201. https://doi.org/10.1007/s11269-021-02877-5
Nagesh Kumar D, Janga Reddy M (2007) Multipurpose reservoir operation using particle swarm optimization. J Water Resour Plan Manag 133(3):192–201. https://doi.org/10.1061/(ASCE)0733-9496(2007)133:3(192)
Nazari A, Deihimi A (2017) MOEICA: Enhanced multi-objective optimization based on imperialist competitive algorithm. Iran J Optim 9(1):21–37
Nikoo MR, Beiglou PHB, Mahjouri N (2016) Optimizing multiple-pollutant waste load allocation in rivers: an interval parameter game theoretic model. Water Resour Manag 30:4201–4220. https://doi.org/10.1007/s11269-016-1415-6
Noory H, Liaghat AM, Parsinejad M, Haddad OB (2012) Optimizing irrigation water allocation and multicrop planning using discrete PSO algorithm. J Irrig Drain Eng 138(5):437–444. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000426
Pashmchi P, Taheriyoun M, Asghari K (2022) Integrated river water quality and quantity management based on undesirability and cost-effectiveness. River Res Appl 38(10):1843–1859. https://doi.org/10.1002/rra.4057
Rahat SH, Steissberg T, Chang W, Chen X, Mandavya G, Tracy J, ... Ray P (2023) Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty. Sci Total Environ 898:165504. https://doi.org/10.1016/j.scitotenv.2023.165504
Szeląg B, Kiczko A, Dąbek L (2019) Stormwater reservoir sizing in respect of uncertainty. Water 11(2):321. https://doi.org/10.3390/w11020321
Talatahari S, Azar BF, Sheikholeslami R, Gandomi AH (2012) Imperialist competitive algorithm combined with chaos for global optimization. Commun Nonlinear Sci Numer Simul 17(3):1312–1319. https://doi.org/10.1016/j.cnsns.2011.08.021
Wu X, Marshall L, Sharma A (2020) Quantifying input uncertainty in the calibration of water quality models: Reshuffling errors via the secant method. Hydrol Earth Syst Sci Discuss 2020:1–26. https://doi.org/10.5194/hess-26-1203-2022
Yandamuri SR, Srinivasan K, Murty Bhallamudi S (2006) Multiobjective optimal waste load allocation models for rivers using nondominated sorting genetic algorithm-II. J Water Resour Plan Manag 132(3):133–143. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:3(133)
Yang J, Lei K, Khu S, Meng W, Qiao F (2015) Assessment of water environmental carrying capacity for sustainable development using a coupled system dynamics approach applied to the Tieling of the Liao River Basin, China. Environ Earth Sci 73:5173–5183. https://doi.org/10.1007/s12665-015-4230-0
Yousefi M, Darus AN, Mohammadi H (2011) Second law based optimization of a plate fin heat exchanger using imperialist competitive algorithm. Int J Phys Sci 6(20):4749–4759. https://doi.org/10.5897/IJPS11.514
Zare Farjoudi S, Moridi A, Sarang A (2021) Multi-objective waste load allocation in river system under inflow uncertainty. Int J Environ Sci Technol 18:1549–1560. https://doi.org/10.1007/s13762-020-02897-5
Zeinali M, Azari A, Heidari MM (2020) Multiobjective optimization for water resource management in low-flow areas based on a coupled surface water–groundwater model. J Water Resour Plan Manag 146(5):04020020. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001189
Zhang Q, Li Z (2021) Data-driven interval credibility constrained quadratic programming model for water quality management under uncertainty. J Environ Manag 293:112791. https://doi.org/10.1016/j.jenvman.2021.112791
Zhang R, Gao H, Zhu W, Hu W, Ye R (2015) Calculation of permissible load capacity and establishment of total amount control in the Wujin River Catchment—a tributary of Taihu Lake, China. Environ Sci Pollut Res 22:11493–11503. https://doi.org/10.1007/s11356-015-4311-3
Zhong B, Wang Z, Yang H, Xu H, Gao M, Liang Q (2023) Parameter optimization of SWMM model using integrated morris and GLUE methods. Water 15(1):149. https://doi.org/10.3390/w15010149
Acknowledgements
The authors wish to acknowledge the financial support from the Iran National Science Foundation.
Funding
This study supported by Iran National Science Foundation and Research office of University of Tabriz under the project number 4002687.
Author information
Authors and Affiliations
Contributions
Omid Babamiri Conceptualization, Methodology, Software, Coding, and Writing Original draft, Visualization.; Yagob Dinpashoh Supervision, Conceptualization, Reviewing and Editing, Validation.
Corresponding author
Ethics declarations
The authors have agreed on submitting this article, and it is not currently under any consideration for reviewing in other journals simultaneously.
Consent to Participate
The authors voluntarily agreed to participate in this research study.
Consent for Publication
The authors approved the publication of this study.
Conflicts of Interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Babamiri, O., Dinpashoh, Y. Uncertainty Analysis of River Water Quality Based on Stochastic Optimization of Waste Load Allocation Using the Generalized Likelihood Uncertainty Estimation Method. Water Resour Manage 38, 967–989 (2024). https://doi.org/10.1007/s11269-023-03704-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-023-03704-9