During the last decades, the Mediterranean region faces increase of mean temperature and decrease of precipitation. In combination with augmented needs in water for irrigation and human consumption, overexploitation of groundwater aquifers has been observed in many Mediterranean basins. The aim of this work is to attempt a fuzzy optimization procedure for groundwater management and specifically for the determination of the optimal pumping rates in the Tympaki coastal aquifer, in Crete, Greece. The intense agricultural production in the area and the consequent overpumping have resulted in saltwater intrusion. The optimization problem has been set as the maximization of the pumping rates, subjected to a set of hydraulic head constraints, in order to push back the saltwater front and simultaneously fulfill water demands. In the first place, the piece-wise linear technique is used and after iterative runs of the simulation – optimization (S – O) procedure, the problem is linearized after the convergence of two consecutive S – O runs. This is the baseline for the assessment of the fuzzy optimization method that is deployed in the next stage. Then, the problem is also expressed as a fuzzy one and the bound and decomposition method for the fully fuzzy linear problems is used in the piece-wise steps. The groundwater system simulation was calibrated according to 2004 – 2008 period of observation data from 6 wells and the runs were based on precipitation data for the ten-year period 2010 – 2020. The pumping wells in the study area are up to 371, which were grouped to 20 to enhance the computational speed of the simulation. The modeling of the groundwater flow is performed with the use of Finite Element subsurface FLOW and transport modelling system (FEFLOW), while the optimization process is executed in Matlab R2017b. It is expected that enhancing results, along with the use of surrogate models, will enable the integration of this technique in a Decision Support System for groundwater management of coastal aquifers. After validation, the same methodology is going to be applied in a second coastal aquifer, Malia, in Crete, Greece. This work was developed under the scope of the InTheMED and Sustain-COAST projects. InTheMED is part of the PRIMA programme supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 1923. Sustain-COAST is funded by the General Secretariat for Research and Innovation of the Ministry of Development and Investments under the PRIMA Programme. PRIMA is an Art.185 initiative supported and co-funded under Horizon 2020, the European Union’s Programme for Research and Innovation.

Optimization Processes for Decision Aiding / Anyfanti, I. V.; Diakoparaskevas, P.; Lyronis, A.; Varouchakis, E.; Karatzas, G. P.; Tanda, M. G.; Zanini, A.; Jomaa, S.. - (2022). ((Intervento presentato al convegno EGU General Assembly tenutosi a Vienna nel 23-27 maggio 2022 [10.5194/egusphere-egu22-11940].

Optimization Processes for Decision Aiding

Tanda M. G.;Zanini A.;
2022-01-01

Abstract

During the last decades, the Mediterranean region faces increase of mean temperature and decrease of precipitation. In combination with augmented needs in water for irrigation and human consumption, overexploitation of groundwater aquifers has been observed in many Mediterranean basins. The aim of this work is to attempt a fuzzy optimization procedure for groundwater management and specifically for the determination of the optimal pumping rates in the Tympaki coastal aquifer, in Crete, Greece. The intense agricultural production in the area and the consequent overpumping have resulted in saltwater intrusion. The optimization problem has been set as the maximization of the pumping rates, subjected to a set of hydraulic head constraints, in order to push back the saltwater front and simultaneously fulfill water demands. In the first place, the piece-wise linear technique is used and after iterative runs of the simulation – optimization (S – O) procedure, the problem is linearized after the convergence of two consecutive S – O runs. This is the baseline for the assessment of the fuzzy optimization method that is deployed in the next stage. Then, the problem is also expressed as a fuzzy one and the bound and decomposition method for the fully fuzzy linear problems is used in the piece-wise steps. The groundwater system simulation was calibrated according to 2004 – 2008 period of observation data from 6 wells and the runs were based on precipitation data for the ten-year period 2010 – 2020. The pumping wells in the study area are up to 371, which were grouped to 20 to enhance the computational speed of the simulation. The modeling of the groundwater flow is performed with the use of Finite Element subsurface FLOW and transport modelling system (FEFLOW), while the optimization process is executed in Matlab R2017b. It is expected that enhancing results, along with the use of surrogate models, will enable the integration of this technique in a Decision Support System for groundwater management of coastal aquifers. After validation, the same methodology is going to be applied in a second coastal aquifer, Malia, in Crete, Greece. This work was developed under the scope of the InTheMED and Sustain-COAST projects. InTheMED is part of the PRIMA programme supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 1923. Sustain-COAST is funded by the General Secretariat for Research and Innovation of the Ministry of Development and Investments under the PRIMA Programme. PRIMA is an Art.185 initiative supported and co-funded under Horizon 2020, the European Union’s Programme for Research and Innovation.
Optimization Processes for Decision Aiding / Anyfanti, I. V.; Diakoparaskevas, P.; Lyronis, A.; Varouchakis, E.; Karatzas, G. P.; Tanda, M. G.; Zanini, A.; Jomaa, S.. - (2022). ((Intervento presentato al convegno EGU General Assembly tenutosi a Vienna nel 23-27 maggio 2022 [10.5194/egusphere-egu22-11940].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2930016
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