Abstract
There has long existed a dichotomy in the field of water resources systems engineering between simulation and optimization modeling, with each approach having its own advantages and disadvantages. Simulation models provide a means of accurately representing the complex physiochemical, socioeconomic, and legal-administrative behavior of complex water resources systems, but lack the capability of systematically determining optimal water planning and management decisions. Optimization models, on the other hand, excel at automatic determination of optima, while often sacrificing the accurate representation of the underlying water system behavior. Various means of effectively establishing a synergy between simulation and optimization models that accentuates their advantages while minimizing their shortcomings have evolved from the field of artificial intelligence within the province of computer science. Artificial intelligence was defined by John McCarthy in 1955 as “the science and engineering of making intelligent decisions.” Machine learning, as a branch of artificial intelligence, focuses on the development of specific algorithms that allow computerized agents to learn optimal behaviors through interaction with a real or simulated environment. Although there are many aspects of machine learning, the focus here is on agent-based modeling tools for learning optimal decisions and management rule structures for water resources systems under conflicting goals and complex stochastic environments. A wide variety of machine learning tools such as reinforcement learning, artificial neural networks, fuzzy rule-based systems, and evolutionary algorithms are applied herein to complex decision problems in integrated management of multipurpose river-reservoir systems, real-time control of combined sewer systems for pollution reduction, and integrated design and operation of stormwater control systems for sustaining and remediating coastal aquatic ecosystems damaged by intensified urbanization and development.
John W. Labadie is former Senior Editor of the ASCE Journal of Water Resources Planning and Management.
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References
Labadie JW, Brazil LE, Corbu I, Johnson LE (eds) (1989) Computerized decision support systems for water managers. American Society of Civil Engineers, Reston, VA
Keys AM, Palmer RN (1995) An assessment of shared vision model effectiveness in water resources planning, Proceedings of the 22nd annual water resources planning and management conference. American Society of Civil Engineers, Washington, DC, pp 532–535
Labadie JW (2004) Optimal operation of multi-reservoir systems: state-of-the-art review. J Water Resour Plann Manage 130(2):93–111
Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour Res 18(3):489–498
Ao S-I, Rieger B, Amouzegar MA (eds) (2010) Machine learning and systems engineering, vol 68, Series: Lecture Notes in Electrical Engineering. Springer, Netherlands
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, MA
World Commission on Dams (WCD) (2000) Dams and development: a new framework for decision-making, report of the world commission on dams. Earthscan Publications Ltd., London
Wang D, Adams BJ (1986) Optimization of real-time reservoir operations with Markov decision processes. Water Resour Res 22(3):345–352
Braga BPF, Yeh WG, Becker L, Barros MTL (1991) Stochastic optimization of multiple reservoir system operation. J Water Resour Plann Manage 117(4):471–481
Tejada-Guibert JA, Johnson SA, Stedinger JR (1995) The value of hydrologic information in stochastic dynamic programming models of a multireservoir system. Water Resour Res 3(10):2571–2579
Lee, J-H, Labadie JW (2007) Stochastic optimization of multi-reservoir systems via reinforcement learning. Water Resour Res 43, No. W11408
Kelman J, Stedinger JR, Cooper LA, Hsu E, Yuan S-Q (1990) Sampling stochastic dynamic programming applied to reservoir operation. Water Resour Res 26(3):447–454
Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artifi Intell Res 4:237–285
Dreyfus SE, Law AM (1977) The art and theory of dynamic programming. Academic, New York, USA
Ross SM (1983) Introduction to stochastic dynamic programming. Academic Press, Inc., San Diego, CA
Watkins C, Dayan P (1992) Technical note: Q-Learning. Mach Learn 8:279–292
Foufoula-Georgiou E (1991) Convex interpolation for gradient dynamic-programming. Water Resour Res 27(1):31–36
Johnson SA, Stedinger JR, Shoemaker CA, Li Y, Tejada-Guibert JA (1993) Numerical-solution of continuous-state dynamic programs using linear and spline interpolation. Oper Res 41(3):484–500
K-water (2003) Geum river basin operational guidelines for MODSIM, technical report. Korea Water Resources Corporation, Daejeon
AMSA (1994) Approaches to combined sewer overflow program development. Association of Metropolitan Sewerage Agencies, Washington, DC
U.S. EPA (1999) Combined sewer overflow management fact sheet. EPA/832/R-99-005, U.S. Environmental Protection Agency, Washington, DC
McCarron J (2010) Chicago Sun Times. August 6
Water News Update (2010) Clean Water Council. http://waternewsupdaate.com, December 29
Loucks ED, Locke EF, Heinz SR, Vitasovic ZC (2004) A real-time control strategy for operating the Milwaukee Metropolitan Sewerage District (MMSD) conveyance and storage system. Proceedings of the 2004 World Water and Environmental Resources Congress: Critical Transitions in Water and Environmental Resources Management, Environmental and Water Resources Institute and American Society of Civil Engineers, Reston, Virginia, USA
Pleau M, Colas H, Lavallée P, Pelletier G, Bonin R (2005) Global optimal real-time control of the Quebec urban drainage system. Environ Model Software 20:401–413
Vazquez J, François M, Gilbert D (2003) Real-time management of a sewage system: verification of the optimality and applicability of graphical linear programming compared to mixed linear programming. J Water Sci 16(4):425–442, article in French
Weyand M (2002) Real-time control in combined sewer systems in Germany–some case studies. Urban Water 4:347–354
Schutze M, Campisano A, Colas H, Schilling W, Vanrolleghem PA (2004) Real-time control of urban wastewater systems–Where do we stand today? J Hydrol 299(3):335–348
Labadie JW (1993) Optimal use of in-line storage for real-time urban stormwater control. In: Cao C, Yen BC, Benedini M (eds) Urban storm drainage. Water Resources Publications, Inc, Highlands Ranch, CO
Darsono S, Labadie JW (2007) Neural optimal control algorithm for real-time regulation of in-line storage in combined sewer systems. Environ Model Software 22:1349–1361
Kayhanian M, Stenstrom MK (2005) First flush pollutant mass loading: Treatment strategies. Trans ResRecord (Hydrology, Hydraulics, and Water Quality), No. 1904, 133–143
Huber WC, Dickinson RE (1992) Stormwater management model. Version 4: User’s Manual, EPA/600/3-88-001a, U.S. Environmental Protection Agency, Athens, Georgia, USA. October
Griva I, Nash SG, Sofer A (2010) Linear and nonlinear optimization. SIAM, Pennsylvania, PA
Chen Y-H, Chai S-Y (1991) UNSTDY: combined sewer model user’s manual. Chen Engineering Technology, Inc, Ft. Collins, CO
Unver OL, Mays LW (1990) Model for real-time optimal flood control operation of a reservoir system. Water Res Manage 4:21–46
Parisini T, Zoppoli R (1994) Neural networks for feedback feed-forward nonlinear control systems. IEEE Trans Neural Netw 5(3):436–449
Haykin S (1994) Neural networks: a comprehensive foundation. IEEE Press, New York
Freeman J (1994) Simulating neural networks with mathematica. Addison Wesley Publishing Company, Inc, Reading, MA
Hassoum M (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge, MA
County K (2004) 2003–2004 Annual combined sewer overflow report. King County Department of Natural Resources and Parks, Wastewater Treatment Division, Seattle
Masters T (1996) Practical neural network recipes in C++. Elsevier Science and Technology Books, Burlington, MA
Wilson C, Scotto L, Scarpa J, Volety A, Laramore S, Haunert D (2005) Survey of water quality, oyster reproduction and oyster health status in the St. Lucie Estuary. J Shellfish Res 24:157–165
USCOE and SFWMD (2004) Central and southern Florida project: Indian river lagoon—south: final integrated project implementation report and environmental impact statement. U.S. Army Corps of Engineers and South Florida Water Management District, Jacksonville, FL
Haunert D, Konyha K (2001) Establishing St. Lucie Estuary Watershed Inflow Targets to enhance Mesohaline Biota, Appendix E., Indian River Lagoon—South Feasibility Study, South Florida Water Management District, West Palm Beach, FL
Wan Y, Labadie J, Konya K, Conboy T (2006) Optimization of frequency distribution of freshwater inflows for coastal ecosystem restoration. J Water Resour Plann Manage 132(5):320–329
Bárdossy A, Duckstein L (1995) Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems. CRC Press, Boca Raton, FL
Zimmermann H (2001) Fuzzy Set theory and its applications. Kluwer, Boston, MA
Holland J (1975) Adaptation in natural and artificial system. The University of Michigan Press, Ann Arbor, MI
Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company Inc, Reading, MA
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, Berlin
Sareni B, Krähenbühl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evolut Comput 2(3):97–106
Bicknell B, Imhoff J, Kittle J, Jobes T, Donigan A (2001) Hydrologic simulation program-FORTRAN, version 12, user’s manual, national exposure research laboratory, office of research and development. U.S. Environmental Protection Agency, Athens, GA
Aqua Terra Consultants (1996) Modifications to HSPF for high water table and Wetlands conditions in South Florida. Report submitted to South Florida Water Management District, West Palm Beach, Florida
Smajstrla AG (1990) Agricultural field scale irrigation requirements simulation (AFSIRS) model, version 5.5. Technical manual. University of Florida, Gainesville, FL
Hu G (1999) Two-dimensional hydrodynamic model of St. Lucie estuary. Proceedings of the ASCE-CSCE national conference on environmental engineering. American Society of Civil Engineers, Reston, VA
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Labadie, J.W. (2014). Advances in Water Resources Systems Engineering: Applications of Machine Learning. In: Wang, L., Yang, C. (eds) Modern Water Resources Engineering. Handbook of Environmental Engineering, vol 15. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-595-8_10
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