CRDEMO: Combined regionalization and dual entropy-multiobjective optimization for hydrometric network design

Abstract

[1] Establishing an adequate hydrometric network to provide accurate and reliable continuous flow information for various users remains a major challenge. This includes the design of new networks or the evaluation of existing networks. This study proposes a combined regionalization and dual entropy-multiobjective optimization (CRDEMO) method for determining minimum network that meets the World Meteorology Organization (WMO) standards, which is considered herein an optimal minimum network. A regionalization approach is used to estimate flows in potential locations for new additional stations, and a dual entropy-multiobjective optimization approach is used to identify optimal trade-offs between the maximum possible information content and the minimum shared information among the stations. The method was examined in two Canadian River basins, namely, the St. John River and St. Lawrence River basins. The St. John River basin has a higher network density compared to the St. Lawrence River basin. Results of the analysis indicate that the St. Lawrence River basin requires a high number of new additional stations to meet the WMO minimum network density. It was also determined that existing stations do not have a significant influence in determining the locations of new stations in the St. Lawrence River basin. Conversely, however, in the St. John River basin, existing stations have significant influence on the determination of locations of new stations due to the higher number of existing stations. Overall, the CRDEMO technique is shown to be robust for designing optimal minimum hydrometric networks and can be a useful tool for evaluating current and proposed networks.Citation: Samuel, J., P. Coulibaly, and J. Kollat (2013), CRDEMO: Combined regionalization and dual entropy-multiobjective optimization for hydrometric network design, Water Resour. Res., 49,[8070][8071][8072][8073][8074][8075][8076][8077][8078][8079][8080][8081][8082][8083][8084][8085][8086][8087][8088][8089]

Entropy Applications to Water Monitoring Network Design: A Review

“…The use of hydrodynamic model enabled to determine the critical monitoring locations in the main stream and its tributaries. On the other hand, Samuel et al [49] combined regionalization techniques with entropy calculation in order to estimate the discharge at candidate locations. They compared the performance of various regionalization methods including not only a conceptual hydrologic model, but also spatial proximity, physical similarity and their combinations with drainage area ratio.…”

“…Unfortunately, the monitoring of soil moisture is very sparse compared to its spatial variability. To design an optimum network for monitoring soil moisture in the Great Lakes Basin, Kornelsen and Coulibaly [36] proposed using data from the Soil Moisture and Ocean Salinity (SMOS) satellite [76] to design a soil moisture monitoring network using the DEMO algorithm of Samuel et al [49]. Grid cells were selected to add monitoring stations that optimally maximized joint entropy while minimizing total correlation using only the satellite data.…”

A methodology for optimal operation of pumping stations in urban drainage systems

“…In order to handle uncertainties in design and operational problems, several methodologies such as fuzzy approach (Fu and Kapelan, 2011), approximating techniques (Afshar et al, 2009) and entropy-based methods (Samuel et al, 2013) have been developed. Among them, Monte Carlo Simulation (MCS), which is also used in this study, is known as the most flexible and reliable method (Shrestha, 2009).…”

Design of an Optimal Soil Moisture Monitoring Network Using SMOS Retrieved Soil Moisture

“…Entropy theory [20] provides a nonparametric measure of the uncertainty of an outcome of a discrete random process and can be used to determine the information content of a particular set of data [19], [20]. The dual-entropy multiobjective optimization (DEMO) system was developed based on the entropy concept for the design of optimal hydrometric networks [21]. DEMO implements the epsilon-dominance hierarchical Bayesian optimization algorithm (ε-hBOA) [22] to find a set of monitoring sites that maximizes the amount of information defined by the joint entropy, while minimizing the shared information content [21].…”

“…The dual-entropy multiobjective optimization (DEMO) system was developed based on the entropy concept for the design of optimal hydrometric networks [21]. DEMO implements the epsilon-dominance hierarchical Bayesian optimization algorithm (ε-hBOA) [22] to find a set of monitoring sites that maximizes the amount of information defined by the joint entropy, while minimizing the shared information content [21]. In order to determine the information content in a potential network, DEMO requires estimates or observations of the geophysical variable for which the network is to be designed at each potential monitoring location.…”