J. Sreekanth

Coupled simulation‐optimization model for coastal aquifer management using genetic programming‐based ensemble surrogate models and multiple‐realization optimization

Approximation surrogates are used to substitute the numerical simulation model within optimization algorithms in order to reduce the computational burden on the coupled simulation-optimization methodology. Practical utility of the surrogate-based simulation-optimization have been limited mainly due to the uncertainty in surrogate model simulations. We develop a surrogate-based coupled simulation-optimization methodology for deriving optimal extraction strategies for coastal aquifer management considering the predictive uncertainty of the surrogate model. Optimization models considering two conflicting objectives are solved using a multiobjective genetic algorithm. Objectives of maximizing the pumping from production wells and minimizing the barrier well pumping for hydraulic control of saltwater intrusion are considered. Density-dependent flow and transport simulation model FEMWATER is used to generate input-output patterns of groundwater extraction rates and resulting salinity levels. The nonparametric bootstrap method is used to generate different realizations of this data set. These realizations are used to train different surrogate models using genetic programming for predicting the salinity intrusion in coastal aquifers. The predictive uncertainty of these surrogate models is quantified and ensemble of surrogate models is used in the multiple-realization optimization model to derive the optimal extraction strategies. The multiple realizations refer to the salinity predictions using different surrogate models in the ensemble. Optimal solutions are obtained for different reliability levels of the surrogate models. The solutions are compared against the solutions obtained using a chance-constrained optimization formulation and single-surrogate-based model. The ensemble-based approach is found to provide reliable solutions for coastal aquifer management while retaining the advantage of surrogate models in reducing computational burden.

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.

Design of an Optimal Compliance Monitoring Network and Feedback Information for Adaptive Management of Saltwater Intrusion in Coastal Aquifers

Management strategies for the optimal and sustainable use of groundwater resources are developed based on prescriptive models that use mathematical tools for simulation and optimization together with field data. Because of the uncertainty inherent in the groundwater systems, it is essential to verify the compliance of the implemented strategies to those prescribed by using proper monitoring techniques during and after the implementation stages of the groundwater management project. In this work, an adaptive management approach for optimal management and monitoring of coastal aquifers is proposed. A simulation-optimization approach is used to derive optimal pumping strategy for the management of saltwater intrusion in coastal aquifers. Then, an optimal monitoring network is designed to evaluate the compliance of the aquifer responses in the field with those predicted by the simulation-optimization model. The designed network can be used to monitor the compliance in the field in terms of the salinity concentration levels, which result from the implementation of the optimal pumping strategy. Uncertainty in the values of groundwater parameters and the uncertainty resulting from the deviation of the pumping strategies from the prescribed optimum values are characterized by considering different realizations of these values in the three-dimensional density dependent flow and transport simulation model. A new objective for monitoring is considered in this study. The objective function consists of maximizing the coefficient of variation of the salinity concentration at the monitored locations and minimizing the correlation coefficient between the concentrations at the monitored locations. Using this objective, the monitoring locations are chosen in regions where the uncertainty in the concentration values is highest, and those locations where the correlation between the concentrations of the monitored locations is lowest, so that the redundancy in monitoring data is the least. The concentration data collected at the optimal compliance monitoring locations can be used as feedback information to improve the initially developed optimal coastal aquifer management strategies. The sequential modification of the optimal pumping strategies in stages is illustrated using numerical experiments.

Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management

[Extract] With the advent of computers a wide range of mathematical and numerical models have been developed with the intent of predicting or approximating parts of hyrdrologic cycle. Prior to the advent of conceptual process based models, physical hydraulic models, which are reduced scale representations of large hydraulic systems, were used commonly in water resources engineering. Fast development in the computational systems and numerical solutions of complex differential equations enabled development of conceptual models to represent physical systems. Thus, in the last two decades large number of mathematical models was developed to represent different processes in hydrological cycle.

Wavelet and cross-wavelet analysis of groundwater quality signals of saltwater intruded coastal aquifers

Monitoring wells are used to collect temporal data on the groundwater table and the salinities. This study uses wavelet analysis for analysing the temporal patterns of groundwater table and salinity levels for a saltwater intruded coastal aquifer. The time series of groundwater table and salinity levels are analysed using continuous wavelet transforms (CWT). Wavelet analysis is able to detect detailed temporal patterns in the time series of groundwater data. These analyses help in improving the water quality monitoring, modeling and determining pumping strategies for contaminated aquifers. Further, phase relationship between the two time series of groundwater table and the salinity levels is analysed using Cross Wavelet Transform (XWT). This analysis is able to provide information on the causative effect of groundwater table on the salinity concentrations along with their phase relationship in time‐frequency domain.