Handling Large Decision Variables in Multi-Objective Groundwater Optimization Problems: Aquifer Parameter-Based Clustering Approach

Shreyansh Mishra, Lilian Bosc, Shishir Gaur (), Mariem Kacem and Anurag Ohri
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Shreyansh Mishra: Indian Institute of Technology (Banaras Hindu University)
Lilian Bosc: Ecole des Mines de Saint-Étienne
Shishir Gaur: Indian Institute of Technology (Banaras Hindu University)
Mariem Kacem: Centrale Lyon-ENISE, Univ Lyon, Tribology and Systems Dynamics Lab. (CNRS UMR 5513 LTDS)
Anurag Ohri: Indian Institute of Technology (Banaras Hindu University)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 11, No 19, 4553-4568

Abstract: Abstract Number of decision variables (DVs) significantly impacts the convergence of multi-objective groundwater simulation-optimization problems (MO-GSOPs). Previous studies of reducing DV by decomposition methods based on the proximity between the pumping wells have yet to assess its implication on the Pareto fronts. This study introduces a novel approach to clustering known as aquifer parameter-based clustering. This work aims to decrease the number of wells involved in MO-GSOPs via clustering based on essential aquifer properties that govern groundwater flow, including initial head, recharge, top elevation of the aquifer layer, and hydraulic conductivity. The simulation-optimization model solves the objectives of maximizing pumping discharge and river-aquifer (R-A) exchanges. The resulting Pareto fronts are compared in terms of convergence and diversity. The analysis reveals that initial head-based clustering exhibits superior performance, leading to a significant increase in hypervolume (46%) and a decrease in the inverted generational distance (22%) compared to distance-based DV clustering. Comparison between results shows that aquifer parameter-based clustering has superior optimal results overall than traditional clustering based upon Euclidian distance. Furthermore, the discharge variation resulting from the parameter-based clustering is examined at the commune level. Notably, Chazey Sur Ain, located near the river, experiences a substantial increase in discharge (12659.13 m3/d), while communes situated near the study area’s boundary, namely Douvres and Jujurieux, observe marginal discharge increases (500 m3/d and 130.33 m3/d, respectively).

Keywords: Multi-objective Simulation-Optimization; Groundwater Management; Clustering; Decision Variable; MOPSO; K-means (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03580-3

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