Sustainable groundwater modeling using single- and multi-objective optimization algorithms

Abstract

This study presents the first attempt to link the multi-algorithm genetically adaptive search method (AMALGAM) with a groundwater model to define pumping rates within a well distributed set of Pareto solutions. The pumping rates along with three minimization objectives, i.e. minimizing shortage affected by the failure to supply, modified shortage index and minimization of extent of drawdown within prespecified regions, were chosen to define an optimal solution for groundwater drawdown and subsidence. Hydraulic conductivity, specific yield parameters of a modular three-dimensional finite-difference (MODFLOW) groundwater model were first optimized using Cuckoo optimization algorithm (COA) by minimizing the sum of absolute deviation between the observed and simulated water table depths. These parameters were then applied in AMALGAM to optimize the pumping rate variables for an arid groundwater system in Iran. The Pareto parameter sets yielded satisfactory results when maximum and minimum drawdowns of the aquifer were defined in a range of −40 to +40 cm/year. Overall, ‘Modelling – Optimization – Simulation’ procedure was capable to compute a set of optimal solutions displayed on a Pareto front. The proposed optimal solution provides sustainable groundwater management alternatives to decision makers in arid region.

Application of MODFLOW with Boundary Conditions Analyses Based on Limited Available Observations: A Case Study of Birjand Plain in East Iran

“…A comprehensive study (e.g., lithology and geology studies) has been done in this research to be able to model the aquifer The aquifer geometry is determined according to the RWCSK data. A comprehensive study (e.g., lithology and geology studies) has been done in this research to be able to model the aquifer boundaries (i.e., aquifer geometry) accurately in the model [35,[56][57][58][59][60][61][62]. The bottom boundary (i.e., the bedrock in the aquifer) is also determined using the limited available geophysical study (geoelectrical soundings) carried out in Birjand Plain in 1971 ( Figure 8).…”

“…Water 2019, 11, 1904 9 of 21 boundaries (i.e., aquifer geometry) accurately in the model [35,[56][57][58][59][60][61][62]. The bottom boundary (i.e., the bedrock in the aquifer) is also determined using the limited available geophysical study (geoelectrical soundings) carried out in Birjand Plain in 1971 ( Figure 8).…”

Real-Time Management of Groundwater Resources Based on Wireless Sensors Networks

“…A few optimization techniques have been used for irrigation management [6,[13][14][15][16]. Sadeghi-Tabas et al proposed an attempt to link MODFLOW to a multi-algorithm genetically adaptive search method (AMALGAM) to optimize pumping rates of a groundwater system in Iran [17]. A surrogate-based approach was developed based on integrated surface water-groundwater modeling to optimize the percentages of surface water and groundwater in irrigation water in order to obtain a better balance between groundwater storage in the middle reaches of HRB and the environmental flow in the lower reaches [18].…”

Decision-Making Challenges of Sustainable Groundwater Strategy under Multi-Event Pressure in Arid Environments: The Diyala River Basin in Iraq

“…Optimization modelling successfully generated future predictions that can be used by decision makers to manage the predicted groundwater shortages in the future.for long-term groundwater monitoring to minimize the budget data loss for inappropriate distribution of sampling locations; Mirghani et al [18] used evolutionary strategies (ES) to identify the source of groundwater contaminants. The authors built a simulation-optimization approach to minimize the root square error between the observed and monitored concentration of pollution in certain observation wells; Ayvaz [19] implemented harmony search (HS) algorithm combined with a simulation model in groundwater management to optimize the pumping rates and costs; Safavi et al [20] coupled simulation and optimization models to minimize the deficit in irrigation water demands using an artificial neural network (ANN) and a genetic algorithm (GA); Piscopo et al [21] implemented MOEA to optimize groundwater remediation by an injection and extraction process; Sreekanth et al [22] implemented the non-dominated sorting genetic algorithm (NSGA-II) to maximize aquifer water injection and to minimize the variance in the water head in the aquifer; Sadeghi-Tabas et al [23] coupled a multi-algorithm genetically adaptive multi-objective (AMALGAM) optimization algorithm and simulation model to minimize the deficit in water demands, shortage index, and the drawdown in the water table; and Lal et al[24] used a multi-objective genetic algorithm (MOGA) to develop a groundwater management model under challenging events.According to the IPCC [25] (Intergovernmental Panel on Climate Change) report, Iraq has an annual precipitation less than 150 mm, located within an arid/or semi-arid environment. Hence, there is conflicting demands for water supply and agricultural use for food security, all in the face of economic challenges.The Iraqi government intends to develop six agricultural projects in the central part of the Diyala river basin in the northeast of Iraq to reinforce the water-food-energy nexus in the country.…”

“…for long-term groundwater monitoring to minimize the budget data loss for inappropriate distribution of sampling locations; Mirghani et al [18] used evolutionary strategies (ES) to identify the source of groundwater contaminants. The authors built a simulation-optimization approach to minimize the root square error between the observed and monitored concentration of pollution in certain observation wells; Ayvaz [19] implemented harmony search (HS) algorithm combined with a simulation model in groundwater management to optimize the pumping rates and costs; Safavi et al [20] coupled simulation and optimization models to minimize the deficit in irrigation water demands using an artificial neural network (ANN) and a genetic algorithm (GA); Piscopo et al [21] implemented MOEA to optimize groundwater remediation by an injection and extraction process; Sreekanth et al [22] implemented the non-dominated sorting genetic algorithm (NSGA-II) to maximize aquifer water injection and to minimize the variance in the water head in the aquifer; Sadeghi-Tabas et al [23] coupled a multi-algorithm genetically adaptive multi-objective (AMALGAM) optimization algorithm and simulation model to minimize the deficit in water demands, shortage index, and the drawdown in the water table; and Lal et al…”