IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v30y2016i4p1395-1413.html
   My bibliography  Save this article

Modelling Intelligent Water Resources Allocation for Multi-users

Author

Listed:
  • Fi-John Chang
  • Yu-Chung Wang
  • Wen-Ping Tsai

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. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Fi-John Chang & Yu-Chung Wang & Wen-Ping Tsai, 2016. "Modelling Intelligent Water Resources Allocation for Multi-users," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1395-1413, March.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:4:p:1395-1413
    DOI: 10.1007/s11269-016-1229-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-016-1229-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-016-1229-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xiaoling Su & Jianfang Li & Vijay Singh, 2014. "Optimal Allocation of Agricultural Water Resources Based on Virtual Water Subdivision in Shiyang River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2243-2257, June.
    2. Asmadi Ahmad & Ahmed El-Shafie & Siti Razali & Zawawi Mohamad, 2014. "Reservoir Optimization in Water Resources: a Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3391-3405, September.
    3. Hamideh Hosseini Safa & Saeed Morid & Mahnoush Moghaddasi, 2012. "Incorporating Economy and Long-term Inflow Forecasting Uncertainty into Decision-making for Agricultural Water Allocation during Droughts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2267-2281, June.
    4. R. Roozbahani & B. Abbasi & S. Schreider & A. Ardakani, 2014. "A Multi-objective Approach for Transboundary River Water Allocation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5447-5463, December.
    5. V. Chandramouli & Paresh Deka, 2005. "Neural Network Based Decision Support Model for Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 447-464, August.
    6. L. Karthikeyan & D. Kumar & Didier Graillot & Shishir Gaur, 2013. "Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 871-883, February.
    7. Neslihan Seckin & Murat Cobaner & Recep Yurtal & Tefaruk Haktanir, 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 Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2103-2124, May.
    8. D. Haro & J. Paredes & A. Solera & J. Andreu, 2012. "A Model for Solving the Optimal Water Allocation Problem in River Basins with Network Flow Programming When Introducing Non-Linearities," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4059-4071, November.
    9. Panuwat Pinthong & Ashim Das Gupta & Mukand Babel & Sutat Weesakul, 2009. "Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 697-720, March.
    10. Erik Porse & Samuel Sandoval-Solis & Belize Lane, 2015. "Integrating Environmental Flows into Multi-Objective Reservoir Management for a Transboundary, Water-Scarce River Basin: Rio Grande/Bravo," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2471-2484, June.
    11. Deepti Rani & Maria Moreira, 2010. "Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1107-1138, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Farhan & Muhammad Asim Yasin & Khuda Bakhsh & Rafaqet Ali & Sami Ullah & Saad Munir, 2022. "Determinants of risk attitude and risk perception under changing climate among farmers in Punjab, Pakistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 2163-2176, November.
    2. Tsai, Wen-Ping & Cheng, Chung-Lien & Uen, Tinn-Shuan & Zhou, Yanlai & Chang, Fi-John, 2019. "Drought mitigation under urbanization through an intelligent water allocation system," Agricultural Water Management, Elsevier, vol. 213(C), pages 87-96.
    3. Zhou, Yanlai & Guo, Shenglian & Chang, Fi-John & Xu, Chong-Yu, 2018. "Boosting hydropower output of mega cascade reservoirs using an evolutionary algorithm with successive approximation," Applied Energy, Elsevier, vol. 228(C), pages 1726-1739.
    4. González Perea, R. & Camacho Poyato, E. & Montesinos, P. & Rodríguez Díaz, J.A., 2018. "Prediction of applied irrigation depths at farm level using artificial intelligence techniques," Agricultural Water Management, Elsevier, vol. 206(C), pages 229-240.
    5. Mahdieh Kalhori & Parisa-Sadat Ashofteh & Seyedeh Hadis Moghadam, 2023. "Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4433-4458, September.
    6. Dan Yan & Saskia E. Werners & He Qing Huang & Fulco Ludwig, 2016. "Identifying and Assessing Robust Water Allocation Plans for Deltas Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5421-5435, November.
    7. Zhou, Yanlai & Guo, Shenglian & Chang, Fi-John & Liu, Pan & Chen, Alexander B., 2018. "Methodology that improves water utilization and hydropower generation without increasing flood risk in mega cascade reservoirs," Energy, Elsevier, vol. 143(C), pages 785-796.
    8. Zhou, Yanlai & Chang, Li-Chiu & Uen, Tin-Shuan & Guo, Shenglian & Xu, Chong-Yu & Chang, Fi-John, 2019. "Prospect for small-hydropower installation settled upon optimal water allocation: An action to stimulate synergies of water-food-energy nexus," Applied Energy, Elsevier, vol. 238(C), pages 668-682.
    9. Xiaojing Shen & Xu Wu & Xinmin Xie & Chuanjiang Wei & Liqin Li & Jingjing Zhang, 2021. "Synergetic Theory-Based Water Resource Allocation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2053-2078, May.
    10. Zhenhui Wu & Yadong Mei & Bei Cheng & Tiesong Hu, 2021. "Use of a Multi-Objective Correlation Index to Analyze the Power Generation, Water Supply and Ecological Flow Mutual Feedback Relationship of a Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 465-480, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fi-John Chang & Yu-Chung Wang & Wen-Ping Tsai, 2016. "Modelling Intelligent Water Resources Allocation for Multi-users," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1395-1413, March.
    2. Behrang Beiranvand & Parisa-Sadat Ashofteh, 2023. "A Systematic Review of Optimization of Dams Reservoir Operation Using the Meta-heuristic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3457-3526, July.
    3. Frederick Chou & Hao-Chih Lee & William Yeh, 2013. "Effectiveness and Efficiency of Scheduling Regional Water Resources Projects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 665-693, February.
    4. Mojtaba Moravej & Seyed-Mohammad Hosseini-Moghari, 2016. "Large Scale Reservoirs System Operation Optimization: the Interior Search Algorithm (ISA) Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3389-3407, August.
    5. Tao Bai & Lianzhou Wu & Jian-xia Chang & Qiang Huang, 2015. "Multi-Objective Optimal Operation Model of Cascade Reservoirs and Its Application on Water and Sediment Regulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2751-2770, June.
    6. Xiaoling Su & Jianfang Li & Vijay Singh, 2014. "Optimal Allocation of Agricultural Water Resources Based on Virtual Water Subdivision in Shiyang River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2243-2257, June.
    7. Seyed-Mohammad Hosseini-Moghari & Reza Morovati & Mohammad Moghadas & Shahab Araghinejad, 2015. "Optimum Operation of Reservoir Using Two Evolutionary Algorithms: Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3749-3769, August.
    8. Panagiotis I. Bakanos & Konstantinos L. Katsifarakis, 2020. "Optimizing Current and Future Hydroelectric Energy Production and Water Uses of the Complex Multi-Reservoir System in the Aliakmon River, Greece," Energies, MDPI, vol. 13(24), pages 1-23, December.
    9. Zhenfang He & Yaonan Zhang & Qingchun Guo & Xueru Zhao, 2014. "Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5297-5317, December.
    10. Alcigeimes Celeste, 2015. "Reservoir Design Optimization Incorporating Performance Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4305-4318, September.
    11. Ahmad, Shahryar Khalique & Hossain, Faisal, 2020. "Maximizing energy production from hydropower dams using short-term weather forecasts," Renewable Energy, Elsevier, vol. 146(C), pages 1560-1577.
    12. Bennie Grové, 2019. "Improved Water Allocation under Limited Water Supplies Using Integrated Soil-Moisture Balance Calculations and Nonlinear Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 423-437, January.
    13. Sabah Fayaed & Ahmed El-Shafie & Othman Jaafar, 2013. "Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3679-3696, August.
    14. Xiaoling Ding & Xiaocong Mo & Jianzhong Zhou & Sheng Bi & Benjun Jia & Xiang Liao, 2021. "Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 645-660, January.
    15. Benyou Jia & Slobodan P. Simonovic & Pingan Zhong & Zhongbo Yu, 2016. "A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3363-3387, August.
    16. Guangyao Deng & Liujuan Wang & Yanan Song, 2015. "Effect of Variation of Water-Use Efficiency on Structure of Virtual Water Trade - Analysis Based on Input–Output Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2947-2965, June.
    17. Feng, Zhong-kai & Niu, Wen-jing & Wang, Wen-chuan & Zhou, Jian-zhong & Cheng, Chun-tian, 2019. "A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy," Energy, Elsevier, vol. 175(C), pages 618-629.
    18. Hu, Zhineng & Chen, Yazhen & Yao, Liming & Wei, Changting & Li, Chaozhi, 2016. "Optimal allocation of regional water resources: From a perspective of equity–efficiency tradeoff," Resources, Conservation & Recycling, Elsevier, vol. 109(C), pages 102-113.
    19. T. Fowe & I. Nouiri & B. Ibrahim & H. Karambiri & J. Paturel, 2015. "OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3841-3861, August.
    20. Thibaut Cuvelier & Pierre Archambeau & Benjamin Dewals & Quentin Louveaux, 2018. "Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Management Under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1599-1614, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:30:y:2016:i:4:p:1395-1413. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.