tuje Turkish Journal of Engineering 2587-1366 Murat YAKAR 10.31127/tuje.1753149 Civil Geotechnical Engineering Numerical Modelization in Civil Engineering Water Resources Engineering Civil Engineering (Other) İnşaat Geoteknik Mühendisliği İnşaat Mühendisliğinde Sayısal Modelleme Su Kaynakları Mühendisliği İnşaat Mühendisliği (Diğer) Implementation of Bat Algorithm (BA) in Vertical Electrical Sounding (VES) Data Inversion https://orcid.org/0000-0002-1529-6268 Farduwin Alhada Sumatera Institute of Technology https://orcid.org/0009-0006-4422-8084 Margareth Lidya Sumatera Institute of Technology https://orcid.org/0000-0002-2185-4131 Antosia Risky Martin Sumatera Institute of Technology https://orcid.org/0000-0002-2185-3104 Rizki Reza Sumatera Institute of Technology https://orcid.org/0000-0002-0891-5745 Styawan Yudha Sumatera Institute of Technology 05 01 2026 10 2 336 350 07 29 2025 03 05 2026 Copyright © 2017, Turkish Journal of Engineering 2017 Turkish Journal of Engineering

Bat Algorithm (BA) is a population-based metaheuristic algorithm inspired by the echolocation mechanism of bats. This algorithm has great potential in solving nonlinear optimization problems, including those encountered during the process of vertical electrical sounding (VES) data inversion for subsurface exploration. This study aims to test the effectiveness and validity of BA in inverting VES data through parameter testing and implementation on synthetic data and field data. The method employed involves tuning the BA parameter to determine the optimal configuration and accuracy testing using both noise-free synthetic data and data with 10% noise added. The results show that the optimal parameter configuration is obtained at an initial loudness value of r_0 = 0.01, a loudness reduction coefficient of α = 0.9, and a pulse emission frequency increase coefficient of γ =0.9. The synthetic data inversion produces a high similarity index (〖SI〗_x) value (above 90%) and low misfit (below 7%), indicating accurate and stable BA performance. The implementation of the BA on field data resulted in low inversion errors, consistently below 4%. Application of the inversion to field data acquired in a residential area in South Lampung successfully identified two aquifer categories: an unconfined aquifer at depths of up to 9 meters and a shallow confined aquifer at depths of 7–43 meters, with tuffaceous sandstone identified as the primary candidate aquifer. This study confirms the effectiveness of BA in interpreting VES data for hydrogeological purposes.

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