Optimal design of groundwater-level monitoring networks
- 10 August 2017
- journal article
- Published by IWA Publishing in Journal of Hydroinformatics
- Vol. 19 (6), 920-929
- https://doi.org/10.2166/hydro.2017.044
Abstract
Groundwater monitoring plays a significant role in groundwater management. This study presents an optimization method for designing groundwater-level monitoring networks. The proposed design method was used in the Eshtehard aquifer, in central Iran. Three scenarios were considered to optimize the locations of the observation wells: (1) designing new monitoring networks, (2) redesigning existing monitoring networks, and (3) expanding existing monitoring networks. The kriging method was utilized to determine groundwater levels at non-monitoring locations for preparing the design data base. The optimization of the groundwater monitoring network had the objectives of (1) minimizing the root mean square error and (2) minimizing the number of wells. The non-dominated sorting genetic algorithm (NSGA-II) was applied to optimize the network. Inverse distance weighting interpolation was used in NSGA-II to estimate the groundwater levels while optimizing network design. Results of the study indicate that the proposed method successfully optimizes the design of groundwater monitoring networks that achieve accuracy and cost-effectiveness.Keywords
This publication has 26 references indexed in Scilit:
- Application of Genetic Programming in Stage Hydrograph Routing of Open ChannelsWater Resources Management, 2013
- Optimal search strategy for the definition of a DNAPL sourceJournal of Hydrology, 2009
- Design of Groundwater Level Monitoring Network with Ordinary KrigingJournal of Hydrodynamics, 2008
- A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertaintyAdvances in Water Resources, 2006
- Support vectors–based groundwater head observation networks designWater Resources Research, 2004
- Assessment of spatial structure of groundwater quality variables based on the entropy theoryHydrology and Earth System Sciences, 2003
- A fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Transactions on Evolutionary Computation, 2002
- Muiltiobjective Optimization Using Nondominated Sorting in Genetic AlgorithmsEvolutionary Computation, 1994
- Review of Ground‐Water Quality Monitoring Network DesignJournal of Hydraulic Engineering, 1992
- A Computer Movie Simulating Urban Growth in the Detroit RegionEconomic Geography, 1970