Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

详细信息    查看全文

• 作者：Ching-Wen Chen (1)
  Chih-Chiang Wei (2)
  Hung-Jen Liu (1)
  Nien-Sheng Hsu (1)
  
• 关键词：Artificial neural networks ; Groundwater ; Network system ; Conjunctive use ; Optimization model ; Chou ; Shui alluvial fan
• 刊名：Water Resources Management
• 出版年：2014
• 出版时间：August 2014
• 年：2014
• 卷：28
• 期：10
• 页码：2813-2832
• 全文大小：1,388 KB
• 参考文献：1. Aquaveo Inc., (2009) GMS Tutorials http://www.aquaveo.com/gms-65tutorials
  2. Bhattacharjya RK, Datta B (2005) Optimal management of coastal aquifers using linked simulation optimization approach. Water Resour Manag 19(3):295鈥?20 CrossRef
  3. Central Geological Survey (2007) Hydrogeology and groundwater resources in Chou-Shui alluvial fan. Conference of Geophysical Environment and Resources, Taiwan (in Chinese)
  4. Changhwa Irrigation Association, Taiwan (2008) Statistics of irrigation condition (in Chinese)
  5. Chen HW, Ning SK, Yu RF, Chen JC (2010) Optimal safe groundwater yield for land conservation in a seashore area under uncertainty. Resour Conserv Recycl 54(8):481鈥?88 CrossRef
  6. Chiang CR, Huang CC, Chen RE (2006) Water budget of Chou-Shui alluvial fan using groundwater hydrograph analysis method. Proceedings No.19, Central Geological Survey (in Chinese)
  7. Chu HJ, Chang LC (2009) Optimal control algorithms and neural network for dynamic groundwater management. Hydrol Process 23:2765鈥?773 CrossRef
  8. Coppola EA, Szidaroszky F, Poulton M, Charles E (2003) Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping and climate conditions. J Hydrol Eng 8(6):348鈥?60 CrossRef
  9. Coppola EA, Szidarovszky F, Davis D, Spayd S, Poulton MM, Roman E (2007) Multiobjective analysis of a public wellfield using artificial neural networks. Ground Water 45(1):53鈥?1 CrossRef
  10. Dhar A, Datta B (2009) Saltwater intrusion management of coastal aquifers. I: linked simulation-optimization. J Hydrol Eng 14(12):1263鈥?272 CrossRef
  11. Ejaz MS, Peralta RC (1995) Maximizing conjunctive use of surface and ground water under surface water quality constraints. Adv Water Resour 18(2):67鈥?5 CrossRef
  12. Emch G, Yeh W-G (1998) Management model for conjunctive use of coastal surface water and groundwater. J Water Resour Plan Manag 124(3):129鈥?39 CrossRef
  13. Gorelick SM (1983) A review of distributed parameter groundwater management modeling methods. Water Resour Res 19(2):305鈥?19 CrossRef
  14. Hsu NS, Cheng KW (2002) Network flow optimization model for basin-scale water supply planning. J Water Resour Plan Manag 128(2):102鈥?12 CrossRef
  15. Ioannis N, Daliakopoulos PC, Ioannis K (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229鈥?40 CrossRef
  16. Karamouz M, Kerchian R, Zahrie B (2004) Monthly water resources and irrigation planning: case study of conjunctive use of surface and groundwater resources. J Irrig Drain Eng 130(5):391鈥?02 CrossRef
  17. Karamouz M, Tabari MMR, Kerachian R (2007) Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water Int 32(1):163鈥?76 CrossRef
  18. Kourakos G, Mantoglou A (2009) Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural networks models. Adv Water Resour 32:507鈥?21 CrossRef
  19. Lin HT, Ke KY, Tan YC, Wu SC, Hsu G, Chen PC, Fang ST (2013) Estimating pumping rates and identifying potential recharge zones for groundwater management in multi-aquifers system. Water Resour Manag 27(9):3293鈥?306 CrossRef
  20. Liu CW (2004) Decision support system for managing ground water resources in the Choushui river alluvial fan in Taiwan. J Am Water Resour Assoc 40(2):431鈥?42 CrossRef
  21. Louie PWF, Yeh W-G, Hsu NS (1984) Multiobjective water resources management planning. J Water Resour Plan Manag 110(1):39鈥?6 CrossRef
  22. McDonald MG, Harbaugh AW (1988) A modular three-dimensional finite-difference groundwater flow model. Washington DC, Science Software Group, Techniques of Water-Resources Investigations of the United States Geological Survey
  23. McPhee J, Yeh W-G (2004) Multiobjective optimization for sustainable groundwater management in semiarid regions. J Water Resour Plan Manag 130(6):490鈥?97 CrossRef
  24. Nikolos IK, Stergiadi M, Papadopoulou MP, Karatzas GP (2008) Artificial neural networks as an alternative approach to groundwater numerical modeling and environmental design. Hydrol Process 22:3337鈥?348 CrossRef
  25. Peralta RC, Azammia H, Takahashi S (1991a) Embedding and response matrix techniques for maximizing steady-state ground-water extraction: computational comparison. Ground Water 29(3):357鈥?64 CrossRef
  26. Peralta RC, Asghari K, Shulstad RN (1991b) SECTAR: model for economically optimal sustained groundwater yield planning. J Irrig Drain Eng 117(IRl):5鈥?4 CrossRef
  27. Pulido-Vel谩zquez M, Andreu J, Sahuquillo A (2006) Economic optimization of conjunctive use of surface water and groundwater at the basin scale. J Water Resour Plan Manag 132(6):454鈥?67 CrossRef
  28. Rao SVN, Bhallamudi SM, Thandaveswara BS, Mishra GC (2004) Conjunctive use of surface and groundwater for coastal and deltaic systems. J Water Resour Plan Manag 130(3):255鈥?67 CrossRef
  29. Safavi HR, Darzi F, Mari帽o MA (2009) Simulation-optimization modeling of conjunctive use of surface water and groundwater. Water Resour Manag 24:1965鈥?988 CrossRef
  30. Singh R, Datta B (2006) Identification of groundwater pollution sources using GA-based linked simulation optimization model. J Hydrol Eng 11(2):101鈥?09 CrossRef
  31. The ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124鈥?37 CrossRef
  32. U.S. Geological Survey (2005) MODFLOW-2005: The U.S. geological survey modular groundwater model- the groundwater flow process. U.S. Geological Survey Techniques and Methods 6-A16
  33. Willis R, Finney B (1988) Planning model for optimal control of saltwater intrusion. J Water Resour Plan Manag 114(2):163鈥?78 CrossRef
  34. Willis R, Liu P (1984) Optimization model for ground鈥搘ater planning. J Water Resour Plan Manag 110(3):333鈥?47 CrossRef
  35. Willis R, Yeh W-G (1987) Groundwater systems planning and management, 1st edn. Prentice Hall Inc., New York
  36. Xu ZX, Li JY (2002) Short-term inflow forecasting using an artificial neural network model. Hydrol Process 16:2423鈥?439 CrossRef
  37. Yang LW, Yu GH (2008) Strategies of agricultural water resources management and improvement of irrigation management. Water Resources Management and Policy Research Center, Taiwan (in Chinese)
  
• 作者单位：Ching-Wen Chen (1)
  Chih-Chiang Wei (2)
  Hung-Jen Liu (1)
  Nien-Sheng Hsu (1)
  
  1. Department of Civil Engineering, National Taiwan University, Taipei, Taiwan, 10617, Republic of China
  2. Department of Information Management, Toko University, Chiayi, Taiwan, 61363, Republic of China
  
• ISSN：1573-1650

文摘

This study develops an optimization model for the large-scale conjunctive use of surface water and groundwater resources. The aim is to maximize public and irrigation water supplies subject to groundwater-level drawdown constraints. Linear programming is used to create the optimization model, which is formulated as a linear constrained objective function. An artificial neural network is trained by a flow modeling program at specific observation wells, and the network is then incorporated into the optimization model. The proposed methodology is applied to the Chou-Shui alluvial fan system, located in central Taiwan. People living in this region rely on large quantities of pumped water for their public and irrigation demands. This considerable dependency on groundwater has resulted in severe land subsidence in many coastal regions of the alluvial fan. Consequently, an efficient means of implementing large-scale conjunctive use of surface water and groundwater is needed to prevent further overuse of groundwater. Two different optimization scenarios are considered. The results given by the proposed model show that water-usage can be balanced with a stable groundwater level. Our findings may assist officials and researchers in establishing plans to alleviate land subsidence problems.