Managing water quality in a river basin with uncertainty

  • H.-W. Chen
  • W.-Y. Chen
  • C.-T. Wang
  • Y.-H. LinEmail author
  • M.-J. Deng
  • C.-Y. Chiang
Original Paper


The effects of both climate change and the geographic location of Taiwan have influenced the perceived variability of river flow and increased uncertainty and complexity in the management of river basins. In this study, a genetic algorithm (GA) optimizer was integrated into a stochastic river basin model to develop a stochastic optimization river basin management model (SORBMM). Firstly, the flow probability density function was determined through statistical analysis of the hydrological data. A Monte Carlo simulation was then conducted to evaluate the effect of flow variability, and a GA was implemented to obtain an optimal river pollution reduction strategy. A true case involving multi-objective management of a river basin under conditions of high spatiotemporal flow variation was tested to demonstrate the feasibility of the SORBMM. The results revealed that a reduction in pollution removal would lead to higher risks for river basin management due to the dilution effect in the river downstream and the objective of lowering pollution removal costs.


Genetic algorithm Monte Carlo simulation Climate change River basin management Uncertainty 



The authors would like to thank the National Science Council in Taiwan (MOST 108-2218-E-224-004-MY3) and Tunghai University Global Research and Education on Environment and Society for their financial support.


  1. Altman DG (1998) Confidence intervals for the number needed to treat. BMJ 317:1309–1312. CrossRefGoogle Scholar
  2. Amelian SS, Sajadi SM, Navabakhsh M, Esmaelian M (2019) Multi-objective optimization of stochastic failure-prone manufacturing system with consideration of energy consumption and job sequences. Int J Environ Sci Technol 16:3389–3402. CrossRefGoogle Scholar
  3. Amin M, Alazba A, ElNesr M (2013) Adaptation of climate variability/extreme in arid environment of the Arabian peninsula by rainwater harvesting and management. Int J Environ Sci Technol 10:27–36CrossRefGoogle Scholar
  4. Baloch M, Ames D, Tanik A (2015) Hydrologic impacts of climate and land-use change on Namnam Stream in Koycegiz Watershed. Turk Int J Environ Sci Technol 12:1481–1494CrossRefGoogle Scholar
  5. Chen HW, Chang N-B (1998) Water pollution control in the river basin by fuzzy genetic algorithm-based multiobjective programming modeling. Water Sci Technol 37:55–63CrossRefGoogle Scholar
  6. Chen H-W, Chang N-B (2010) Using fuzzy operators to address the complexity in decision making of water resources redistribution in two neighboring river basins. Adv Water Resour 33:652–666. CrossRefGoogle Scholar
  7. Crevillén-García D, Wilkinson RD, Shah AA, Power H (2017) Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media. Adv Water Resour 99:1–14. CrossRefGoogle Scholar
  8. de Medeiros IC, da Costa Silva JFCB, Silva RM, Santos CAG (2019) Run-off–erosion modelling and water balance in the Epitácio Pessoa Dam river basin, Paraíba State in Brazil. Int J Environ Sci Technol 16:3035–3048. CrossRefGoogle Scholar
  9. Freeman B, Gharabaghi B, Thé J (2019) Estimating annual air emissions from nargyla water pipes in cafés and restaurants using Monte Carlo analysis. Int J Environ Sci Technol 16:2539–2548. CrossRefGoogle Scholar
  10. Havens KE, Schelske CL (2001) The importance of considering biological processes when setting total maximum daily loads (TMDL) for phosphorus in shallow lakes and reservoirs. Environ Pollut 113:1–9CrossRefGoogle Scholar
  11. Karmakar S, Mujumdar PP (2007) A two-phase grey fuzzy optimization approach for water quality management of a river system. Adv Water Resour 30:1218–1235CrossRefGoogle Scholar
  12. Kasiviswanathan KS, Sudheer KP (2013) Quantification of the predictive uncertainty of artificial neural network based river flow forecast models. Stoch Environ Res Risk Assess 27:137–146. CrossRefGoogle Scholar
  13. Keppel G (1991) Design and analysis: a researcher’s handbook. Prentice-Hall Inc., Upper Saddle RiverGoogle Scholar
  14. Ketabchi H, Ataie-Ashtiani B (2015) Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges. J Hydrol 520:193–213. CrossRefGoogle Scholar
  15. Kunstmann H, Kastens M (2006) Direct propagation of probability density functions in hydrological equations. J Hydrol 325:82–95. CrossRefGoogle Scholar
  16. Li YP, Huang GH (2009) Fuzzy-stochastic-based violation analysis method for planning water resources management systems with uncertain information. Inf Sci 179:4261–4276CrossRefGoogle Scholar
  17. Lin Y-H, Chen Y-P, Yang M-D, Su T-C (2016) Multiobjective optimal design of sewerage rehabilitation by using the nondominated sorting genetic algorithm-II. Water Resour Manag 30:487–503. CrossRefGoogle Scholar
  18. Liolios KA, Moutsopoulos KN, Tsihrintzis VA (2012) Modeling of flow and BOD fate in horizontal subsurface flow constructed wetlands. Chem Eng J 200:681–693CrossRefGoogle Scholar
  19. Liu Y, Yang P, Hu C, Guo H (2008) Water quality modeling for load reduction under uncertainty: a Bayesian approach. Water Res 42:3305–3314CrossRefGoogle Scholar
  20. Mehr AD, Kahya E, Şahin A, Nazemosadat M (2015) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol 12:2191–2200CrossRefGoogle Scholar
  21. Mejía A, Rossel F, Gironás J, Jovanovic T (2015) Anthropogenic controls from urban growth on flow regimes. Adv Water Resour 84:125–135. CrossRefGoogle Scholar
  22. Muzik I (2002) A first-order analysis of the climate change effect on flood frequencies in a subalpine watershed by means of a hydrological rainfall–runoff model. J Hydrol 267:65–73CrossRefGoogle Scholar
  23. Ning SK, Chang N-B, Yang L, Chen HW, Hsu HY (2001) Assessing pollution prevention program by QUAL2E simulation analysis for the Kao-Ping River Basin. Taiwan J Environ Manag 61:61–76. CrossRefGoogle Scholar
  24. Noh SJ, Lee S, An H, Kawaike K, Nakagawa H (2016) Ensemble urban flood simulation in comparison with laboratory-scale experiments: impact of interaction models for manhole, sewer pipe, and surface flow. Adv Water Resour 97:25–37. CrossRefGoogle Scholar
  25. Novotny EV, Stefan HG (2007) Stream flow in Minnesota: indicator of climate change. J Hydrol 334:319–333CrossRefGoogle Scholar
  26. Palisade-Corporation (2015) @Risk, Verison 6.
  27. Qin H, Jiang J, Fu G, Zheng Y (2013) Optimal water quality management considering spatial and temporal variations in a tidal river. Water Resour Manag 27:843–858. CrossRefGoogle Scholar
  28. Roider EM, Adrian DD (2007) Comparative evaluation of three river water quality models1. JAWRA J Am Water Resour Assoc 43:322–333CrossRefGoogle Scholar
  29. Taiwan Environmental Water Quality Information Database (2019)
  30. Volpi E, Di Lazzaro M, Fiori A (2012) A simplified framework for assessing the impact of rainfall spatial variability on the hydrologic response. Adv Water Resour 46:1–10. CrossRefGoogle Scholar
  31. Wang YY, Huang GH, Wang S, Li W, Guan PB (2016) A risk-based interactive multi-stage stochastic programming approach for water resources planning under dual uncertainties. Adv Water Resour 94:217–230. CrossRefGoogle Scholar
  32. Xu Y-P, Booij MJ, Tong Y-B (2010) Uncertainty analysis in statistical modeling of extreme hydrological events. Stoch Environ Res Risk Assess 24:567–578. CrossRefGoogle Scholar
  33. Yang M-D, Chen Y-P, Lin Y-H, Ho Y-F, Lin J-Y (2016) Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus. Energy Build 122:120–130CrossRefGoogle Scholar
  34. Yang M-D, Lin M-D, Lin Y-H, Tsai K-T (2017) Multiobjective optimization design of green building envelope material using a non-dominated sorting genetic algorithm. Appl Therm Eng 111:1255–1264CrossRefGoogle Scholar
  35. Zhang J, Liu P, Wang H, Lei X, Zhou Y (2015) A Bayesian model averaging method for the derivation of reservoir operating rules. J Hydrol 528:276–285. CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.Department of Environmental Science and EngineeringTunghai UniversityTaichung 407Taiwan
  2. 2.Department of Chemical and Materials EngineeringNational Yunlin University of Science and TechnologyDouliouTaiwan
  3. 3.Bachelor Program in Interdisciplinary StudiesNational Yunlin University of Science and TechnologyDouliouTaiwan

Personalised recommendations