Linking GIS, Hydraulic Modeling, and Tabu Search for Optimizing a Water Level-Monitoring Network in South Florida

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 n stations, the methodology involves searching for the best spatial combination of r (where r<n) 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.