Optimal management of a theoretical coastal aquifer with combined pollution and salinization problems, using genetic algorithms

Kontos, Yiannis; Katsifarakis, Konstantinos

doi:10.1016/j.energy.2016.10.035

Cited by 16 publications

(9 citation statements)

References 21 publications

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“…These approaches can be analytical (Katsifarakis 2008), or numerical, using Evolutionary Algorithms and other similar methods (Kougias and Theodossiou 2013). Moreover, there are some approaches dealing with pumping's environmental impact minimization (Kontos and Katsifarakis 2017;Lim and Park 2008).…”

Section: Introductionmentioning

confidence: 99%

Reducing Groundwater Pumping Cost Using Alternate Pulsed Pumping

Nagkoulis¹

2020

Groundwater

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This paper deals with water pumping cost minimization, in a confined infinite aquifer, proposing an alternate pulsed pumping schedule. The transient flow analysis is conducted for two wells with equal pumping rates. Specifically, two pumping schedules are analytically compared. In the first case, well users pump simultaneously, and in the second one they cooperate so that they pump alternately. This paper proves that the proposed alternate pumping schedule works as a stabilizer, reducing the high hydraulic drawdowns values, regardless of the aquifer characteristics. Moreover, pumping alternately is better in terms of pumping cost, compared to simultaneous pumping, though benefit become negligible as distance between wells becomes large. Two simplified equations are proposed, one to find the difference of the hydraulic drawdowns between the two pumping schedules and the other one to find the economic benefit of each well from cooperation. The equations are finally applied to a number of cases and their results are compared to the results obtained from an algorithm created to calculate the hydraulic drawdowns and the pumping cost, using the Theis equation. The results can be very useful in irrigation scheduling, as they can be applied to systems of well users/farmers, to reduce pumping cost.

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Section: Introductionmentioning

confidence: 99%

Reducing Groundwater Pumping Cost Using Alternate Pulsed Pumping

Nagkoulis¹

2020

Groundwater

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“…Zhang et al [24] applied genetic algorithms and non-linear optimization to corn irrigation considering stages of crop growth, the grain market price, irrigation water price, minimum yield, and irrigation cost etc. Kontos and Katsifarakis [25] employed genetic algorithms for irrigation and drinking water management for coastal aquifer. Jimenez et al [26] employed Long Short-Term Memory Neural Network for precision irrigation considering different soil types.…”

Section: Irrigation Management Models Based On Mathematical Programmingmentioning

confidence: 99%

Multistage stochastic programming modeling for farmland irrigation management under uncertainty

2020

PLoS ONE

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Farmland management and irrigation scheduling are vital to a productive agricultural economy. A multistage stochastic programming model is proposed to maximize farmers' annual profit under uncertainty. The uncertainties considered include crop prices, irrigation water availability, and precipitation. During the first stage, pre-season decisions including seed type and plant density are made, while determinations of when to irrigate and how much water to be used for each irrigation are made in the later stages. The presented case study, based on a farm in Nebraska, U.S.A., showed that a 10% profit increase could be achieved by taking the corn price and irrigation water availability uncertainties into consideration using two-stage stochastic programming. An additional 13% profit increase could be achieved by taking precipitation uncertainty into consideration using multistage stochastic programming. The stochastic model outperforms the deterministic model, especially when there are limited water supplies. These results indicate that multistage stochastic programming is a promising method for farm-scale irrigation management and can increase farm profitability.

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“…Such sources can be leaking underground fuel tanks, landfills, septic systems, hazardous waste sites and leaks or spills of industrial chemicals at manufacturing facilities. Cost of groundwater pollution control and remediation methods, like pump-and-treat or hydraulic control, is high; its minimization is essential yet challenging [ 1 , 2 ]. Moreover, when the cleanup process includes the simultaneous protection of operating pumping wells providing drinking or/and irrigation water, the complexity and level of required intervention increase.…”

Section: Introductionmentioning

confidence: 99%

“…Three simulation series are implemented (Table 5 ): (a) Sim1 investigates source identification for a conservative pollutant, simulating advective (only) mass transport with “MovPo” (a tool of the “OptiManage” optimization software created by some of the authors [ 1 , 2 ]); contaminant release of one of the potential sources is assumed instantaneous; (b) Sim2 investigates the same problem, but simulating advection–dispersion mass transport with Modflow software of the USGS suite [ 69 ]; (c) Sim3 resembles Sim2, but also considering unknown contaminant release duration.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for groundwater pollution source identification and monitoring network optimization

Kontos

Kassandros

Perifanos

et al. 2022

Neural Comput & Applic

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The identification of the source in groundwater pollution is the only way to drastically deal with resulting environmental problems. This can only be achieved by an appropriate monitoring network, the optimization of which is prerequisite for the solution of the inverse modeling problem, i.e., identifying the source of the pollutant on the basis of measurements taken within the pollution field. For this reason, a theoretical confined aquifer with two pumping wells and six suspected sources is studied. Simulations of combinations of possible source locations, and hydraulic parameters, produce sets of measurement features for a 29 × 29 grid representing potential monitoring wells. Three sets of simulations are conducted to produce synthetic datasets, representing different groundwater pollution modeling methods. Features (input- X variables) coupled with respective sources (output- Y variables) are formulated in two different dataset formats (Types A, B) in order to train classification (random forests, multilayer perceptron) and computer vision (convolutional neural networks) algorithms, respectively, to solve the inverse modeling problem. In addition, appropriate feature selection and trial-and-error tests are employed for supporting the optimization of monitoring wells’ number, locations and sampling frequency. The methodology can successfully produce various sub-optimal monitoring strategies for various budgets. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-022-07507-8.

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Optimal management of a theoretical coastal aquifer with combined pollution and salinization problems, using genetic algorithms

Cited by 16 publications

References 21 publications

Reducing Groundwater Pumping Cost Using Alternate Pulsed Pumping

Reducing Groundwater Pumping Cost Using Alternate Pulsed Pumping

Multistage stochastic programming modeling for farmland irrigation management under uncertainty

Machine learning for groundwater pollution source identification and monitoring network optimization

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