Optimum identification of aquifer parameter zone structures using the SHuffled Ant Lion Optimization approach considering general form of the Voronoi tessellation

• Published in: Journal of hydrology (Amsterdam) 2024-08, Vol.640, p.131683, Article 131683
• Main Authors: Tozak, Mirac Bugse, Durgut, Pinar G., Ayvaz, M. Tamer
• Format: Article
• Language: English
• Subjects: Groundwater Parameter Structure Identification; Shuffled Ant Lion Optimization; Simulation-Optimization; Voronoi Tessellation
• ISSN: 0022-1694
• DOI: 10.1016/j.jhydrol.2024.131683

•A new approach is proposed for groundwater parameter structure identification.•Parameter structures are determined using the general form of the VT approach.•Minkowski distance is employed to assign the data points into the VT zones.•The heuristic SHALO approach is first applied to the solution of inverse problems.•Model identification performance has significantly enhanced. A new simulation–optimization approach is proposed for solving the inverse parameter structure identification problems in groundwater systems. In the simulation part of the proposed approach, the numerical groundwater flow process is simulated by using MODFLOW. Parameter structures of the aquifer system are identified by partitioning the flow domain into a finite number of zones employing the general form of the Voronoi tessellation (VT). This MODFLOW and VT-based simulation model is then integrated into an optimization model where the SHuffled Ant Lion Optimization approach (SHALO) is used. This work is the first application of SHALO by considering the general form of the VT zonation approach for solving the inverse groundwater parameter structure identification problems. The applicability of the proposed simulation–optimization approach is evaluated on a hypothetical aquifer model in the literature. This evaluation is conducted by solving the same problem using Ant Lion Optimization (ALO) and its improved version, the self-adaptive ALO (saALO) for the same conditions. Furthermore, the identified results are compared with those obtained using harmony search (HS) and hybrid genetic algorithm (hybrid GA) based solution approaches in the literature. The obtained results indicated that the proposed approach provided better identification results than ALO, saALO, HS, and hybrid GA in terms of different evaluation metrics.