Multi-Objective Operations of Multi-Wetland Ecosystem: iModel Applied to the Everglades Restoration
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 9
Abstract
The Everglades is a complex, multiwetland ecosystem that is heavily managed to meet often-competing flood control, water supply, and environmental demands. Using objective measures to balance these demands through operational protocols has always been a challenge in the multibillion-dollar restoration plans for the ecosystem. Physically based models have been the primary tools for planning efforts but for such a complex system, they are laborious and computationally intensive. Development of optimal operations based on iterative runs of these models is a great challenge. This paper presents an inverse modeling framework for formal optimization suited for wetland system operations that helps overcome such limitations. Labor-intensive and computation-intensive physically representative models are emulated in each individual wetland area using an autoregressive artificial neural network with exogenous variables. Using prescribed inflow, outflow, and meteorological input data, such hydrologic model emulators aided by a dimension-reduction technique provide targeted spatial and temporal predictions for water level (stage) within each area of the Everglades, while excluding computation processes that are intensive but insignificant to the predictions. This computer software uses the augmented Lagrangian genetic algorithm technique (subject to linear and nonlinear constraints) to steer predictions of stage spatial variability within individual wetlands towards corresponding desired goals (including restoration targets). In the augmented Lagrangian genetic algorithm, flow releases are coded as the decision variables to be optimized subject to budget, intrahydraulic conveyance, flow capacity, and upstream storage constraints. Optimization is performed by dividing and solving a sequence of subproblems using the genetic algorithm procedures of initialization, selection, elitism, crossover, and mutation. As part of the process, Lagrangian and penalty parameters are updated, and optimization terminates when certain stopping criteria are met. Applying the technique reported in this paper to a specific Everglades restoration plan (the River of Grass Project) showed a sound hydrologic model emulator prediction when compared to the physical model for all wetland areas. Feeding optimal releases predicted by the computer software into a physical model showed equal or better matching of the restoration target with different release patterns compared to that of the physical model base run scenario. Results show that hydraulic conveyance limitations play a significant role in Everglades restoration. Also, results show that employing an adversity tradeoff matrix presented multiple so-called optimal solutions with different optimization weights and a powerful negotiation matrix.
Get full access to this article
View all available purchase options and get full access to this article.
References
Ali, A. (2009). “Nonlinear multivariate rainfall–stage model for large wetland systems.” J. Hydrol., 374(3–4), 338–350.
Ali, A., Neidrauer, C., Cadavid, L., Linton, P., Tarboton, K., and Obeysekera, J. (2006). “New rainfall-based management plan for more natural water deliveries to the Everglades Shark River Slough, operating reservoirs in changing conditions.” Proc., Operations Management 2006 Conf., ASCE, Reston, VA.
Azamathulla, H., et al. (2008). “Genetic programming to predict ski-jump bucket spill-way scour.” J. Hydrodyn., 20(4), 477–484.
Blanning, R. (1975). “The construction and implementation of metamodels.” Simulation, 24(6), 177–184.
Cancelliere, A., Ancarani, A., Giuliano, G., and Rossi, G. (2002). “A neural networks approach for deriving irrigation reservoir operating rules.” Water Resour. Manage., 16(1), 71–88.
Chang, F. J., Chang, L. C., and Huang, H. L. (2002). “Real time recurrent learning neural network for stream-flow forecasting.” Hydrol. Process., 16(13), 2577–2588.
Chang, F. J., and Chen, L. (1998). “Real-coded genetic algorithm for rule-based flood control reservoir management.” Water Resour. Manage., 12(3), 185–198.
Conn, A. R., Gould, N. I. M., and Toint, P. L. (1997). “A globally convergent Lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds.” Math. Comput., 66(217), 261–289.
Dai, T., and Labadie, J. W. (2001). “River basin network model for integrated water quantity/quality management.” J Water Resour. Plann. Manage., 295–305.
Dhar, A., and Datta, B. (2008). “Optimal operation of reservoirs for downstream water quality control using linked simulation optimization.” Hydrol. Process., 22(6), 842–853.
Eberhart, R. C., and Shi, Y. (2001). “Particle swarm optimization: Developments, applications and resources.” Proc., IEEE Conf. on Evolutionary Computation, Soul, South Korea.
El-Shafie, A., Noureldin, A. E., Taha, M. R., and Basri, H. (2008). “Neural network model for Nile River inflow forecasting based on correlation analysis of historical inflow data.” J. Appl. Sci., 8(24), 4487–4499.
Esat, V., and Hall, M. J. (1994). “Water resources system optimisation using genetic algorithms.” Proc., 1st Int. Conf. on Hydroinformatics, Vol. 1, International Association for Hydraulic Research, 225–231.
Galelli, S., Gandolfi, C., Soncini-Sessa, R., and Agostani, D. (2010). “Building a metamodel of an irrigation district distributed-parameter model.” Agric. Water Manage., 97(2), 187–200.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Reading, MA.
Haupt, R. L., and Haupt, S. E. (2004). Practical genetic algorithms, WIley, Hoboken, NJ.
Haykin, S. (1994). Neural networks: A comprehensive foundation, Macmillan, New York.
Hossain, M. S., and El-Shafie, A. (2013). “Intelligent systems in optimizing reservoir operation policy: A review.” Water Resour. Manage., 27(9), 3387–3407.
Hsu, K., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall–runoff process.” Water Resour. Res., 31(10), 2517–2530.
Johnson, V., and Rogers, L. (2000). “Accuracy of neural network approximators in simulation-optimization.” J. Water Resour. Plann. Manage., 48–56.
Kerachian, R., and Karamouz, M. (2006). “Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach.” Water Resour. Res., 42(12), W12401.
Kern, J., Characklis, G., Doyle, M., Blumsack, S., and Whisnant, R. (2012). “Influence of deregulated electricity markets on hydropower generation and downstream flow regime.” J. Water Resour. Plann. Manage., 342–355.
Kim, T., and Heo, J.-H. (2004). “Multireservoir system optimization using multi-objective genetic algorithms.” Proc., 2004 World Water and Environmental Resources Congress: Critical Transitions in Water and Environmental Resources Management, ASCE, Reston, VA, 1900–1909.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). “Optimization by simulated annealing.” Science, 220(4598), 671–680.
Labadie, J. (2004). “Optimal operation of multireservoir systems: State-of-the-art review.” J. Water Resour. Plann. Manage., 93–111.
Lund, J. R., and Guzman, J. (1999). “Derived operating rules for reservoirs in series or in parallel.” J. Water Resour. Plann. Manage., 143–153.
Mobley, J. T., Culver, T. B., and Hall, T. E. (2013). “Simulation-optimization methodology for the design of outlet control structures for ecological detention ponds.” J. Water Resour. Plann. Manage., 04014031.
Nicklow, J., et al. (2010). “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage., 412–432.
Oliveira, R., and Louckas, D. (1997). “Operating rules for multireservoir systems.” Water Resour. Res., 33(4), 839–852.
Rajurkar, M. P., Kothyari, U. C., and Chaube, U. C. (2004). “Modeling of the daily rainfall–runoff relationship with artificial neural network.” J. Hydrol., 285(1–4), 96–113.
Rani, D., and Moreira, M. (2010). “Simulation-optimization modeling: A survey and potential application in reservoir systems operation.” Water Resour. Manage., 24(6), 1107–1138.
Ranjithan, S. R. (2005). “Role of evolutionary computation in environmental and water resources systems analysis.” J. Water Resour. Plann. Manage., 1–2.
Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., and Kollat, J. B. (2013). “Evolutionary multiobjective optimization in water resources: The past, present, and future.” Adv. Water Resour., 51, 438–456.
Regional Simulation Model [Computer software]. West Palm Beach, FL, South Florida Water Management District.
Reichold, L., Zechman, E. M., Brill, E. D., and Holmes, H. (2010). “Simulation-optimization framework to support sustainable watershed development by mimicking the predevelopment flow regime.” J. Water Resour. Plann. Manage., 366–375.
Sajikumar, N., and Thandaveswara, B. S. (1999). “A nonlinear rainfall runoff model using an artificial neural network.” J. Hydrol., 216(1–2), 32–55.
Sharif, M., and Wardlaw, R. (2000). “Multireservoir systems optimization using genetic algorithms: Case study.” J. Comput. Civ. Eng., 255–263.
South Florida Water Management District. (2005). Regional simulation model–Theory manual, West Palm Beach, FL.
South Florida Water Management District. (2010). Science workshop, River of Grass project planning, phase II, West Palm Beach, FL.
Teegavarapu, R. S. V., and Simonovic, S. P. (2000). “Short-term operation model for coupled hydropower reservoirs.” J. Water Resour. Plann. Manage., 98–106.
Wasserman, P. D. (1989). Neural computing: Theory and practice, Van Nostrand Reinhold, New York.
Wurbs, R. A. (1993). “Reservoir-system simulation and optimization models.” J. Water Resour. Plann. Manage., 455–472.
Yeh, W. (1985). “Reservoir management and operations models: A state-of-the-art review.” Water Resour. Res., 21(12), 1797–1818.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 141 • Issue 9 • September 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Feb 10, 2014
Accepted: Dec 5, 2014
Published online: Jan 30, 2015
Discussion open until: Jun 30, 2015
Published in print: Sep 1, 2015
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.