Surrogate assisted multi-objective robust optimization for groundwater monitoring network design

Song, Jian; Yang, Yun; Gan, Chen; Sun, Xiaomin; Jiang, Lin; Wu, Jianfeng

doi:10.1016/j.jhydrol.2019.123994

Cited by 26 publications

(4 citation statements)

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“…Practical requirements often warrant design or optimization of a network to monitor multiple parameters including water levels and different water quality parameters. Applying optimization approaches for such monitoring network design would need including of several variable types in simulation and optimization analysis and often require multi-objective optimization approaches when objectives are mutually conflicting in nature (Kollat and Reed, 2006;Reed et al, 2007;Luo et al, 2016;Bode et al, 2018;Song et al, 2019;Fan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical based optimization of groundwater monitoring well network design

Gladish,

Pagendam,

Janardhanan

et al. 2023

Front. Earth Sci.

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Monitoring groundwater quality in economically important and other aquifers is carried out regularly as part of regulatory processes for water and other resource development. Many water quality parameters are measured as part of baseline monitoring around mining and onshore gas resource development regions to develop improved understanding of the hydrogeological system as well as to inform managerial decisions to assess and manage contamination risks and health hazards. Water quality distribution in an aquifer is most often inferred from point measurements from limited number of bores drilled at arbitrary locations. Estimating the distribution of water quality parameters in the aquifer based on these point measurements is often a challenging task and results in high uncertainty in the estimates due to limited data availability. Minimizing uncertainty can be achieved by drilling more bores to collect water quality data and several approaches are available to identify optimal bore hole locations to minimize estimation uncertainty. However, optimization of borehole locations is difficult when multiple water quality parameters are of interest and have different spatial distributions in the aquifer. In this study we use geostatistical kriging to interpolate a large number of groundwater quality parameters. Then we integrate these predicted values and use the Differential Evolution algorithm to determine optimal locations for bores that would simultaneously reduce spatial prediction uncertainty of all parameters. The method is applied for designing a groundwater monitoring network in the Namoi region of Australia for monitoring groundwater quality in an economically important aquifer of the Great Artesian Basin. Optimal locations for 10 new monitoring bores are identified using this approach.

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Section: Introductionmentioning

confidence: 99%

Geostatistical based optimization of groundwater monitoring well network design

Gladish,

Pagendam,

Janardhanan

et al. 2023

Front. Earth Sci.

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“…Currently, data collection in subsurface studies is primarily guided by intuition and expert knowledge or practical considerations, rather than by the systematic study of which data introductions can help reduce prediction uncertainty (de Barros et al., 2009; Kikuchi et al., 2015). Previous studies have focused more on the optimization of monitoring networks, that is, how to reasonably set the location and number of monitoring points so that more valuable data are obtained at a minimum cost (Dai et al., 2016; Farlin et al., 2019; Song et al., 2019). Some studies have investigated the effect of different observation types on subsurface structure identification.…”

Section: Introductionmentioning

confidence: 99%

Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

Zhan

Dai

Soltanian

et al. 2022

Water Resources Research

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“…The goal of this paper is to develop a framework for designing monitoring networks in heterogeneous aquifer systems. It contributes not only a better understanding of processes controlling groundwater behaviors, but eliminates the unnecessary costs associated with research activities (Carrera et al., 1984; McPhee & Yeh, 2006; Reed & Kollat, 2013; Song et al., 2019; Ushijima & Yeh, 2013, 2015).…”

Section: Introductionmentioning

confidence: 99%

Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems

Chen

Dai

Dong

et al. 2022

Water Resources Research

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Groundwater monitoring networks are direct sources of information for revealing subsurface system dynamic processes. However, designing such networks is difficult due to uncertainties in the spatial heterogeneity of aquifer parameters such as permeability (k). This study combines deep learning and information theory with an optimization framework to address network design problems in heterogeneous aquifer systems. The framework first employs a generative adversarial network to parameterize heterogeneous k distribution using a low-dimensional latent representation. Then, surrogate models are developed based on the deep neural networks to perform uncertainty quantification of pressure heads and solute concentrations at locations of pre-designed candidate monitoring stations. The monitoring stations are then ranked using the greedy search algorithm based on the maximum information minimum redundancy (MIMR) criterion. In order to depict the importance of each candidate monitoring location, the hotspot maps of the selection probability (P s ) are derived from MIMR repetition results. Comprehensive monitoring networks derived from the hotspot maps are then conducted as the final monitoring stations to improve monitoring information compared to MIMR results. Additionally, nine entropy quantization strategies are compared to evaluate their effects on monitoring network optimization results. Results indicate that caution should be taken when selecting entropy quantization strategies to achieve the accuracy required for model calibration and to improve the efficiency of monitoring optimization. Considering high-dimensional uncertainties associated with aquifer parameters, the developed framework can provide important insights for monitoring network designs in various earth observational projects. Plain Language SummaryGroundwater is widely distributed throughout the crust and lithosphere playing a critical role in geological evolution processes and environmental problems. This study presents an integrated framework for designing groundwater monitoring networks in heterogeneous aquifer systems. The proposed model takes advantage of the strength of deep-learning algorithms and deals with high-dimensional and highly non-linear problems of entropy-based methods for designing the monitoring networks. The framework can efficiently provide monitor locations for complex aquifers. The monitoring data collected by the designed network can significantly reduce uncertainties associated with high-dimensional parameters and contain the least amount of redundant information. Further, the proposed method is significant in practice as credible groundwater forecasting requires model calibrations that use monitoring data.

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Surrogate assisted multi-objective robust optimization for groundwater monitoring network design

Cited by 26 publications

References 50 publications

Geostatistical based optimization of groundwater monitoring well network design

Geostatistical based optimization of groundwater monitoring well network design

Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework

Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems

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