Skip to main content

Advertisement

SpringerLink
Log in

Modelling Intelligent Water Resources Allocation for Multi-users

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation.

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
Fig. 5

Similar content being viewed by others

References

  • Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40

    Article  Google Scholar 

  • Ahmad A, El-Shafie A, Razali AFM, Mohamad ZS (2014) Reservoir optimization in water resources: a review. Water Resour Manag 28:3391–3405

    Article  Google Scholar 

  • Chandramouli V, Deka P (2005) Neural network based decision support model for optimal reservoir operation. Water Resour Manag 19:447–464

    Article  Google Scholar 

  • Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10

    Article  Google Scholar 

  • Chang FJ, Chen PA, Lu YR, Huang E, Chang KY (2014) Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. J Hydrol 517:836–846

    Article  Google Scholar 

  • Chang FJ, Wang KW (2013) A systematical water allocation scheme for drought mitigation. J Hydrol 507:124–133

    Article  Google Scholar 

  • Chang LC, Chang FJ (2009) Multi-objective evolutionary algorithm for operating parallel reservoir system. J Hydrol 377:12–20

    Article  Google Scholar 

  • Chang LC, Chang FJ, Wang KW, Dai SY (2010) Constrained genetic algorithms for optimizing multi-use reservoir operation. J Hydrol 390:66–74

    Article  Google Scholar 

  • Chang LC, Chen PA, Chang FJ (2012) A reinforced two-step-ahead weight adjustment technique for on-line training of recurrent neural networks. IEEE Trans Neur Netw Learn 23:1269–1278

    Article  Google Scholar 

  • Chen PA, Chang LC, Chang FJ (2013) Reinforced recurrent neural networks for multi-step-ahead flood forecasts. J Hydrol 497:71–79

    Article  Google Scholar 

  • Chen YH, Chang FJ (2009) Evolutionary artificial neural networks for hydrological systems forecasting. J Hydrol 367:125–137

    Article  Google Scholar 

  • Chiang YM, Chang LC, Tsai MJ, Wang YF, Chang FJ (2010) Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites. Hydrol Earth Syst Sci 14(7):1309–1319

    Article  Google Scholar 

  • Choong SM, El-Shafie A (2014) State-of-the-art for modelling reservoir inflows and management optimization. Water Resour Manag 29:1267–1282

    Article  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    Google Scholar 

  • Elferchichi A, Gharsallah O, Nouiri I, Lebdi F, Lamaddalena N (2009) The genetic algorithm approach for identifying the optimal operation of a multi-reservoirs on-demand irrigation system. Biosyst Eng 102:334–344

    Article  Google Scholar 

  • Haro D, Paredes J, Solera A, Andreu J (2012) A model for solving the optimal water allocation problem in river basins with network flow programming when introducing non-linearities. Water Resour Manag 26(14):4059–4071

    Article  Google Scholar 

  • Hasebe M, Nagayama Y (2002) Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Adv Eng Softw 33:245–260

    Article  Google Scholar 

  • Hong Y, Chiang YM, Liu Y, Hsu KL, Sorooshian S (2006) Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map. Int J Remote Sens 27(23):5165–5184

    Article  Google Scholar 

  • Karthikeyan L, Kumar DN, Graillot D, Gaur S (2013) Prediction of ground water levels in the uplands of a tropical coastal riparian wetland using artificial neural networks. Water Resour Manag 27(3):871–883

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I — a discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Nayak PC, Venkatesh B, Krishna B, Jain SK (2013) Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach. J Hydrol 493:57–67

    Article  Google Scholar 

  • Nourani V, Ejlali RG, Alami MT (2011) Spatiotemporal groundwater level forecasting in coastal 39 aquifers by hybrid artificial neural network-geostatistics model: a case study. Environ Eng Sci 28(3):217–228

    Article  Google Scholar 

  • Pinthong P, Gupta AD, Babel MS, Weesakul S (2009) Improved reservoir operation using hybrid genetic algorithm and neurofuzzy computing. Water Resour Manag 23:697–720

    Article  Google Scholar 

  • Porse EC, Sandoval-Solis S, Lane BA (2015) Integrating environmental flows into multi-objective reservoir management for a transboundary, Water-Scarce River Basin: Rio Grande/Bravo. Water Resour Manag 29:2471–2484

    Article  Google Scholar 

  • Rani D, Moreira MM (2010) Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24:1107–1138

    Article  Google Scholar 

  • Roozbahani R, Abbasi B, Schreider S, Ardakani A (2014) A multi-objective approach for transboundary river water allocation. Water Resour Manag 28:5447–5463

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart, McClelland (eds) Parallel distributed processing, vol 1, Foundations. MIT Press, Cambridge, pp 318–335

    Google Scholar 

  • Safa HH, Morid S, Moghaddasi M (2012) Incorporating economy and long-term inflow forecasting uncertainty into decision-making for agricultural water allocation during droughts. Water Resour Manag 26:2267–2281

    Article  Google Scholar 

  • Seckin N, Cobaner M, Yurtal R, Haktanir T (2013) Comparison of artificial neural network methods with L-moments for estimating flood flow at ungauged sites: the case of East Mediterranean River Basin, Turkey. Water Resour Manag 27(7):2103–2124

    Article  Google Scholar 

  • Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248

    Article  Google Scholar 

  • Su XL, Li JF, Singh VP (2014) Optimal allocation of agricultural water resources based on virtual water subdivision in Shiyang River Basin. Water Resour Manag 28:2243–2257

    Article  Google Scholar 

  • Tsai MJ, Abrahart RJ, Mount NJ, Chang FJ (2014) Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan. Hydrol Process 28:1055–1070

    Article  Google Scholar 

  • Wang KW, Chang LC, Chang FJ (2011) Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Adv Water Resour 34(10):1343–1351

    Article  Google Scholar 

  • Yilmaz AG, Imteaz MA, Jenkins G (2011) Catchment flow estimation using artificial neural networks in the mountainous Euphrates Basin. J Hydrol 410(1–2):134–140

    Article  Google Scholar 

  • Zahraie B, Hosseini SM (2009) Development of reservoir operation policies considering variable agricultural water demands. Expert Syst Appl 36(3):4980–4987

    Article  Google Scholar 

Download references

Acknowledgments

This study was partially supported by the Council of Agriculture, Taiwan, R.O.C. (Grant number: 103AS-8.2.6-IE-b1 (5)). The authors feel much gratitude toward the Editors and Reviewers for their valuable comments and constructive suggestions.

Author information

Authors and Affiliations

  1. Department of Bioenvironmental System Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Da-An District, Taipei, 10617, Taiwan, Republic of China

    Fi-John Chang, Yu-Chung Wang & Wen-Ping Tsai

Authors
  1. Fi-John Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-Chung Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-Ping Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fi-John Chang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, FJ., Wang, YC. & Tsai, WP. Modelling Intelligent Water Resources Allocation for Multi-users. Water Resour Manage 30, 1395–1413 (2016). https://doi.org/10.1007/s11269-016-1229-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-016-1229-6

Keywords

Search

Navigation