This study develops the water resources management model for conjunctive use of surface and subsurface water using a fuzzy inference system (FIS). The study applies the FIS to allocate the demands of surface and subsurface water. Subsequently, water allocations in the surface water system are simulated by using linear programming techniques, and the responses of subsurface water system with respect to pumping are forecasted by using artificial neural networks. The operating rule for the water systems is that the more abundant water system supplies more water. By using the fuzzy rule, the FIS conjunctive use model easily incorporates expert knowledge and operational polices into water resources management. The result indicates that the FIS model is more effective and efficient when compared with the decoupled conjunctive use and simulation-optimization models. Furthermore, the FIS model is an alternative way to obtain the conjunctive use policies between surface and subsurface water. 1. Introduction The objective of the study is to develop a fuzzy rule-based method for the conjunctive use of surface and subsurface water systems. Zadeh [1] applied the fuzzy theory to mathematically deal with the imprecision and uncertainty. Fuzzy logic extends upon traditional Boolean logic and deals with the imprecision in human experience [2]. The fuzzy inference system (FIS) is an artificial intelligence technique that combines the fuzzy set, fuzzy logic, and fuzzy reasoning [1, 3–6]. The FIS utilizes linguistic variables, fuzzy rules, and fuzzy reasoning and provides a tool for knowledge representation based on degrees of membership [7]. During the past decade, the FIS application ranged from runoff forecasting to surface water supply [3–6, 8, 9]. Shrestha et al. [3] developed a FIS to determine a real-world reservoir operation. They constructed a fuzzy rule-based model to derive operation rules for a multipurpose reservoir. Their research used reservoir storages, estimated inflows, and demands as the premises and took reservoir releases as the consequences. The finding showed that the fuzzy-based structure was ordinary and time-saving in computation. Russell and Campbell [4] developed the FIS for a simplified hydroelectric reservoir. The results also showed that fuzzy logic seemingly offered a way to improve the existing operating practices, which was relatively easy to explain and understand when compared with the complex optimization model. Panigrahi and Mujumdar [5] used a FIS for a reservoir operation model. The study incorporated expert knowledge for framing the
References
[1]
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[2]
M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, Reading, Mass, USA, 2002.
[3]
B. P. Shrestha, L. Duckstein, and E. Z. Stakhiv, “Fuzzy rule-based modeling of reservoir operation,” Journal of Water Resources Planning and Management, vol. 122, no. 4, pp. 262–268, 1996.
[4]
S. O. Russell and P. F. Campbell, “Reservoir operating rules with fuzzy programming,” Journal of Water Resources Planning and Management, vol. 122, no. 3, pp. 165–170, 1996.
[5]
D. P. Panigrahi and P. P. Mujumdar, “Reservoir operation modelling with fuzzy logic,” Water Resources Management, vol. 14, no. 2, pp. 89–109, 2000.
[6]
M. ？zger, “Comparison of fuzzy inference systems for streamflow prediction,” Hydrological Sciences Journal, vol. 54, no. 2, pp. 261–273, 2009.
[7]
H. Chu and L. Chang, “Application of optimal control and fuzzy theory for dynamic groundwater remediation design,” Water Resources Management, vol. 23, no. 4, pp. 647–660, 2009.
[8]
C. Mahabir, F. E. Hicks, and A. R. Fayek, “Application of fuzzy logic to forecast seasonal runoff,” Hydrological Processes, vol. 17, no. 18, pp. 3749–3762, 2003.
[9]
O. Kisi, M. E. Karahan, and Z. ？en, “River suspended sediment modelling using a fuzzy logic approach,” Hydrological Processes, vol. 20, no. 20, pp. 4351–4362, 2006.
[10]
H. Ba？a？ao？lu and M. A. Mari？o, “Joint management of surface and ground water supplies,” Ground Water, vol. 37, no. 2, pp. 214–222, 1999.
[11]
R. C. Peralta, R. R. A. Cantiller, and J. E. Terry, “Optimal large-scale conjuctive water-use planning: case study,” Journal of Water Resources Planning & Management, vol. 121, no. 6, pp. 471–478, 1995.
[12]
C. R. Philbrick and P. K. Kitanidis, “Optimal conjunctive-use operations and plans,” Water Resources Research, vol. 34, no. 5, pp. 1307–1316, 1998.
[13]
S. V. N. Rao, S. M. Bhallamudi, B. S. Thandaveswara, and G. C. Mishra, “Conjunctive use of surface and groundwater for coastal and deltaic systems,” Journal of Water Resources Planning and Management, vol. 130, no. 3, pp. 255–267, 2004.
[14]
D. W. Watkins and D. C. McKinney, “Decomposition methods for water resources optimization models with fixed costs,” Advances in Water Resources, vol. 21, no. 4, pp. 283–295, 1998.
[15]
T. Nishikawa, “Water-resources optimization model for Santa Barbara, California,” Journal of Water Resources Planning and Management, vol. 124, no. 5, pp. 252–263, 1998.
[16]
M. N. Azaiez, “A model for conjunctive use of ground and surface water with opportunity costs,” European Journal of Operational Research, vol. 143, no. 3, pp. 611–624, 2002.
[17]
E. Coppola, Optimal pumping policy for a public supply wellfield using computational neural network with decision-making methodology [Ph.D. thesis], University of Arizona at Tucson, Tucson, Ariz, USA, 2000.
[18]
E. Coppola, M. Poulton, E. Charles, J. Dustman, and F. Szidarovszky, “Application of artificial neural networks to complex groundwater management problems,” Natural Resources Research, vol. 12, no. 4, pp. 303–320, 2003.
[19]
E. Coppola, F. Szidarovszky, M. Poulton, and E. Charles, “Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions,” Journal of Hydrologic Engineering, vol. 8, no. 6, pp. 348–359, 2003.
[20]
S. Feng, S. Kang, Z. Huo, S. Chen, and X. Mao, “Neural networks to simulate regional ground water levels affected by human activities,” Ground Water, vol. 46, no. 1, pp. 80–90, 2008.
[21]
H. Chu and L. Chang, “Optimal control algorithm and neural network for dynamic groundwater management,” Hydrological Processes, vol. 23, no. 19, pp. 2765–2773, 2009.
[22]
A. W. Minns and M. J. Hall, “Artificial neural networks as rainfall-runoff models,” Hydrological Sciences Journal, vol. 41, no. 3, pp. 399–417, 1996.
[23]
H. R. Maier and G. C. Dandy, “Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications,” Environmental Modelling and Software, vol. 15, no. 1, pp. 101–124, 2000.
[24]
J. Morshed and J. J. Kaluarachchi, “Application of artificial neural network and genetic algorithm in flow and transport simulations,” Advances in Water Resources, vol. 22, no. 2, pp. 145–158, 1998.
[25]
M. Tu, N. Hsu, and W. W.-G. Yeh, “Optimization of reservoir management and operation with hedging rules,” Journal of Water Resources Planning and Management, vol. 129, no. 2, pp. 86–97, 2003.
[26]
J. W. Labadie, “Optimal operation of multireservoir systems: state-of-the-art review,” Journal of Water Resources Planning and Management, vol. 130, no. 2, pp. 93–111, 2004.
[27]
Hydrologic Engineering Center (HEC), HEC-5 Simulation of Flood Control and Conservation Systems: User's Manual. Version 8, US Army Corps of Engineers, Davis, Calif, USA, 1998.
[28]
C. Wei and N. Hsu, “Multireservoir real-time operations for flood control using balanced water level index method,” Journal of Environmental Management, vol. 88, no. 4, pp. 1624–1639, 2008.
[29]
MATLAB, Neural Network Tool Box for Use with Matlab: User Guide. Version 4, vol. 3, The Mathwork, Apple Hill Drive, Mass, USA, 2000.
[30]
L. W. Mays and Y. K. Tung, Hydrosystems Engineering and Management, McGraw-Hill, New York, NY, USA, 1992.
[31]
Hydrologic Engineering Center (HEC), Reservoir Yield, Generalized Computer Program 23-J2-L245, US Army Corps of Engineers, Davis, Calif, USA, 1966.
[32]
Hydrologic Engineering Center (HEC), Hydrologic Engineering Methods for Water Resources Development, Volume 8, Reservoir Yield, US Army Corps of Engineers, Davis, Calif, USA, 1975.
[33]
K. Ponnambalam, F. Karray, and S. J. Mousavi, “Minimizing variance of reservoir systems operations benefits using soft computing tools,” Fuzzy Sets and Systems, vol. 139, no. 2, pp. 451–461, 2003.
[34]
J. W. Labadie, “Optimal operation of multireservoir systems: state-of-the-art review,” Journal of Water Resources Planning and Management, vol. 130, no. 2, pp. 93–111, 2004.
[35]
D. C. McKinney and M. -D. Lin, “Genetic algorithm solution of groundwater management models,” Water Resources Research, vol. 30, no. 6, pp. 1897–1906, 1994.
[36]
L. Chen, J. McPhee, and W. W.-G. Yeh, “A diversified multiobjective GA for optimizing reservoir rule curves,” Advances in Water Resources, vol. 30, no. 5, pp. 1082–1093, 2007.
[37]
L. Chang, H. Chu, and C. Hsiao, “Optimal planning of a dynamic pump-treat-inject groundwater remediation system,” Journal of Hydrology, vol. 342, no. 3-4, pp. 295–304, 2007.
[38]
A. W. Harbaugh and M. G. McDonald, “Programmer's documentation for MODFLOW-96, an update to the U.S. Geological Survey modular finite-difference ground-water flow model,” Open-File Report 96-486, U.S. Geological Survey, Reston, Va, USA, pp. 220, 1996.
[39]
P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization, Academic Press, New York, NY, USA, 1981.
[40]
O. G. Adrian, D. L. Roudolph, and J. A. Cherry, “Analysis of long-term land subsidence near Mexico City: field investigations and predictive modeling,” Water Resources Research, vol. 35, no. 11, pp. 3327–3341, 1999.
[41]
N. C. Don, N. T. M. Hang, H. Araki, H. Yamanishi, and K. Koga, “Groundwater resources and management for paddy field irrigation and associated environmental problems in an alluvial coastal lowland plain,” Agricultural Water Management, vol. 84, no. 3, pp. 295–304, 2006.
[42]
K. Ying, S. Lin, Z. Lee, and I.-L. Lee, “A novel function approximation based on robust fuzzy regression algorithm model and particle swarm optimization,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 1820–1826, 2011.
