Modelling Intelligent Water Resources Allocation for Multi-users

详细信息    查看全文

• 作者：Fi-John Chang ; Yu-Chung Wang ; Wen-Ping Tsai
• 关键词：Water allocation ; Multi ; objective reservoir operation ; Artificial neural network (ANN) ; Non ; dominated sorting genetic algorithm ; II (NSGA ; II) ; Back ; propagation neural network (BPNN) ; Adaptive network fuzzy inference system (ANFIS)
• 刊名：Water Resources Management
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：30
• 期：4
• 页码：1395-1413
• 全文大小：2,719 KB
• 参考文献：Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40CrossRef
  Ahmad A, El-Shafie A, Razali AFM, Mohamad ZS (2014) Reservoir optimization in water resources: a review. Water Resour Manag 28:3391–3405CrossRef
  Chandramouli V, Deka P (2005) Neural network based decision support model for optimal reservoir operation. Water Resour Manag 19:447–464CrossRef
  Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10CrossRef
  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 517:836–846CrossRef
  Chang FJ, Wang KW (2013) A systematical water allocation scheme for drought mitigation. J Hydrol 507:124–133CrossRef
  Chang LC, Chang FJ (2009) Multi-objective evolutionary algorithm for operating parallel reservoir system. J Hydrol 377:12–20CrossRef
  Chang LC, Chang FJ, Wang KW, Dai SY (2010) Constrained genetic algorithms for optimizing multi-use reservoir operation. J Hydrol 390:66–74CrossRef
  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 23:1269–1278CrossRef
  Chen PA, Chang LC, Chang FJ (2013) Reinforced recurrent neural networks for multi-step-ahead flood forecasts. J Hydrol 497:71–79CrossRef
  Chen YH, Chang FJ (2009) Evolutionary artificial neural networks for hydrological systems forecasting. J Hydrol 367:125–137CrossRef
  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):1309–1319CrossRef
  Choong SM, El-Shafie A (2014) State-of-the-art for modelling reservoir inflows and management optimization. Water Resour Manag 29:1267–1282CrossRef
  Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester
  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 102:334–344CrossRef
  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):4059–4071CrossRef
  Hasebe M, Nagayama Y (2002) Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Adv Eng Softw 33:245–260CrossRef
  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):5165–5184CrossRef
  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):871–883CrossRef
  Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I — a discussion of principles. J Hydrol 10(3):282–290CrossRef
  Nayak PC, Venkatesh B, Krishna B, Jain SK (2013) Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach. J Hydrol 493:57–67CrossRef
  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):217–228CrossRef
  Pinthong P, Gupta AD, Babel MS, Weesakul S (2009) Improved reservoir operation using hybrid genetic algorithm and neurofuzzy computing. Water Resour Manag 23:697–720CrossRef
  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 29:2471–2484CrossRef
  Rani D, Moreira MM (2010) Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24:1107–1138CrossRef
  Roozbahani R, Abbasi B, Schreider S, Ardakani A (2014) A multi-objective approach for transboundary river water allocation. Water Resour Manag 28:5447–5463CrossRef
  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
  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 26:2267–2281CrossRef
  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):2103–2124CrossRef
  Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248CrossRef
  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 28:2243–2257CrossRef
  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 28:1055–1070CrossRef
  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):1343–1351CrossRef
  Yilmaz AG, Imteaz MA, Jenkins G (2011) Catchment flow estimation using artificial neural networks in the mountainous Euphrates Basin. J Hydrol 410(1–2):134–140CrossRef
  Zahraie B, Hosseini SM (2009) Development of reservoir operation policies considering variable agricultural water demands. Expert Syst Appl 36(3):4980–4987CrossRef
  
• 作者单位：Fi-John Chang (1)
  Yu-Chung Wang (1)
  Wen-Ping Tsai (1)
  
  1. Department of Bioenvironmental System Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Da-An District, Taipei, 10617, Taiwan, Republic of China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650

文摘

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. Keywords Water allocation Multi-objective reservoir operation Artificial neural network (ANN) Non-dominated sorting genetic algorithm-II (NSGA-II) Back-propagation neural network (BPNN) Adaptive network fuzzy inference system (ANFIS)