Abstract
The South Florida Water Management District (SFWMD) has a large and expanding surface water level-monitoring network for various water bodies including lakes and streams. SFWMD seeks to optimize the existing network of monitoring stations to collect the data from the least number of monitoring stations in the best possible locations without compromising information. Tabu combinatorial search algorithm is used to optimize water level-monitoring stations in lakes and streams within the SFWMD. Given a network of stations, the methodology involves searching for the best spatial combination of (where ) monitoring stations within the existing network to estimate water surface levels in lakes and streams within a given tolerance. Separate techniques are used to compute water levels in lakes and streams to compare possible solutions from Tabu search with observed daily data. Results from application of proposed techniques to Kissimmee River basin show that implementation of Tabu search within geographic information system (GIS) provides a computationally efficient way of optimizing a water level-monitoring network. It is also found that factors such as length of data records, slope of hydraulic profiles, and data at control structures play a significant role in the overall optimization process. The proposed methodology can also be used in the design of new monitoring networks and other water resources applications that involve GIS, computational modeling, and optimization.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study was supported by the South Florida Water Management District. The collaboration of SFWMD staff and, in particular, Dr. Chandra Pathak, is gratefully acknowledged. The writers also thank three anonymous reviewers and the journal editor for providing comments which led to significant improvement of the manuscript.
References
Barnes, J. W., and Chambers, J. B. (1995). “Solving the job shop scheduling problem with tabu search.” IIE Trans., 27, 257–263.
Barnes, J. W., Dokov, S., Dimova, B., and Solomon, A. (2003). “A theory of elementary landscapes.” Appl. Math. Lett., 16, 337–343.
Battiti, R., and Tecchiolli, G. (1994). “The reactive Tabu search.” OGRSA J. Computing, 6(2), 126–140.
Carlton, W. B., and Barnes, J. W. (1996). “A note on hashing functions and tabu search algorithms.” Eur. J. Oper. Res., 95, 237–239.
Caruso, C., and Quarta, F. (1998). “Interpolation methods comparison.” Comput. Math. Appl., 35(12), 109–126.
Cieniawski, S. E., Eheart, J. W., and Ranjithan, S. (1995). “Using genetic algorithms to solve a multiobjective groundwater monitoring problem.” Water Resour. Res., 31(2), 399–409.
Collins, F. C., and Bolstad, P. V. (1996). “A comparison of spatial interpolation techniques in temperature estimation.” Proc., 3rd Int. Conf.: Workshop on Integrating GIS and Environmental Modeling (CD-ROM), National Center for Geographic Information Analysis (NCGIA), Santa Fe, N.M.
Dessalegne, T., and Nicklow, J. W. (2003). “Evolutionary algorithms for optimal control of the Illinois waterway.” Proc., World Water & Environmental Resources Congress 2003, ASCE, Reston, Va.
Dessalegne, T., and Nicklow, J. W. (2004). “Optimal operation of multi-reservoir river systems using an artificial life algorithm.” Proc., World Water & Environmental Resources Congress 2004, ASCE, Reston, Va.
DHI Water and Environment. (2005). DHI Water & Environment’s website, ⟨http://www.dhi.dk/⟩ (Nov. 16, 2005).
Dixon, W., Smyth, G. K., and Chiswell, B. (1999). “Optimized selection of river sampling sites.” Water Res., 33(4), 971–978.
Erxleben, J., Elder, K., and Davis, R. (2002). “Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains.” Hydrolog. Process., 16, 3627–3649.
ESRI. (2004). ArcGIS desktop help. ArcGIS desktop, version 9.0, ESRI, Redlands, Calif.
Gen, M., and Cheng, R. (2000). Genetic algorithms & engineering optimization, Wiley, New York, 53–96.
Glover, F. (1989). “Tabu search. Part I.” ORSA J. Comput., 1(2), 190–206.
Glover, F. (1995). “Tabu search. Fundamentals and uses, revised and expanded.” Univ. of Colorado, ⟨http://leeds-faculty.colorado.edu/glover/TS%20-%20Fundamentals&Uses.pdf⟩ (Dec. 24, 2009).
Glover, F., and Laguna, M. (1993). “Tabu search.” Modern heuristic techniques for combinatorial problems, C. R. Reeves, ed., Blackwell Science, Oxford, U.K., 70–150.
Hartkamp, A. D., De Beurs, K., Stein, A., and White, J. W. (1999). Interpolation techniques for climate variables, NRG-GIS series 99-01, CIMMYT, Mexico.
HEC-RAS river analysis system; user’s manual, version 3.1.2. (2004). U.S. Army Corps of Engineers, Hydrologic Engineering Center, Davis, Calif.
Laguna, M., and Barnes, J. W. (1991). “Tabu search methods for a single machine scheduling problem.” J. Intell. Manuf., 2, 63–73.
Lee, Y. M., and Ellis, J. H. (1996). “Comparison of algorithms for nonlinear integer optimization: Application to monitoring network design.” J. Environ. Eng., 122(6), 524–531.
Maidment, D. R. (2002). Arc Hydro-GIS for water resources, ESRI, Redlands, Calif., 203.
Martinez, S. I., and Maidment, D. R. (2006). “Stage-monitoring network optimization using GIS.” Center for Research in Water Resources online Rep. No. 06-9, Univ. of Texas at Austin, Austin, Tex., ⟨http://www.crwr.utexas.edu/reports/2006/rpt06-9.shtml⟩ (Dec. 24, 2009).
Merwade, V. M., and Maidment, D. R. (2004). “Geospatial description of river channels in three dimensions.” Center for Research in Water Resources online Rep. No. 04-8, Univ. of Texas at Austin, Austin, Tex., ⟨http://www.crwr.utexas.edu/reports/2004/rpt04-8.shtml⟩ (Dec. 24, 2009).
Minder, E., and Nicklow, J. W. (2001). “System-wide optimization of dam operations to control water level fluctuations.” Proc., World Water & Environmental Resources Congress 2001, ASCE, Reston, Va.
National Oceanic and Atmospheric Administration (NOAA). (2005). “HL river mechanics.” NOAA’s National Weather Service, ⟨http://www.nws.noaa.gov/oh/hrl/rvrmech/⟩ (Nov. 23, 2005).
Nunes, L. M., Cunha, M. C., and Ribeiro, L. (2004a). “Groundwater monitoring network optimization with redundancy reduction.” J. Water Resour. Plann. Manage., 130(1), 33–43.
Nunes, L. M., Paralta, E., Cunha, M. C., and Ribeiro, L. (2004b). “Groundwater nitrate monitoring network optimization with missing data.” Water Resour. Res., 40, W02406.
Pardo-Igúzquiza, E. (1998). “Optimal selection of number and location of rainfall gauges for aerial rainfall estimation using geostatistics and simulated annealing.” J. Hydrol., 210, 206–220.
Reed, P. M., and Minsker, B. S. (2004). “Striking the balance: Long-term groundwater monitoring design for conflicting objectives.” J. Water Resour. Plann. Manage., 130(2), 140–149.
Reed, P. M., Minsker, B. S., and Valocchi, A. J. (2000). “Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation.” J. Water Resources Research, 36(12), 3731–3741.
Sárközy, F. (1999). “GIS functions—Interpolation.” Periodica Polytechnica Ser. Civ. Eng., 43(1), 63–86.
Sorenson, J. K., and Maidment, D. R. (2004). “Temporal geoprocessing for hydroperiod analysis of the Kissimmee River.” Center for Research in Water Resources online Rep. No. 04-5, Univ. of Texas at Austin, Austin, Tex., ⟨http://www.crwr.utexas.edu/reports/2004/rpt04-5.shtml⟩ (Dec. 24, 2009).
South Florida Water Management District (SFWMD). (2004). ⟨https://my.sfwmd.gov/portal/page?_pageid=2754,19862620&_dad=portal&_schema=PORTAL⟩ (Dec. 24, 2009).
South Florida Water Management District (SFWMD). (2008). “Appendix 2–1: Hydrologic monitoring network of the South Florida Water Management.” 2008 South Florida Environmental Rep., South Florida Water Management District, West Palm Beach, Fla., ⟨https://my.sfwmd.gov/pls/portal/docs/PAGE/PG_GRP_SFWMD_SFER/PORTLET_SFER/TAB2236041/VOLUME1/vol1_table_of_contents.html⟩ (Dec. 24, 2009).
Zimmerman, D., Pavlik, C., Ruggles, A., and Armstrong, M. P. (1999). “An experimental comparison of ordinary and universal kriging and inverse distance weighting.” Math. Geol., 31(4), 375–390.
Information & Authors
Information
Published In
Copyright
© 2010 ASCE.
History
Received: Aug 5, 2008
Accepted: Jan 29, 2009
Published online: Mar 27, 2009
Published in print: Mar 2010
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.