A Novel Optimisation Framework for the Interpretation of Unconfined Aquifer Pumping Test Data

ŞAHİN A. U.

Proceedings of the Institution of Civil Engineers: Water Management, cilt.177, sa.2, ss.61-74, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 177 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1680/jwama.21.00115
  • Dergi Adı: Proceedings of the Institution of Civil Engineers: Water Management
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, Geobase, ICONDA Bibliographic, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.61-74
  • Anahtar Kelimeler: groundwater, hydrology &water resources, numerical methods, porous-media characterisation
  • Hacettepe Üniversitesi Adresli: Evet

Özet

© 2022 ICE Publishing: All rights reserved.The complex well function formulations developed for the unconfined aquifer systems make the determination of aquifer parameters difficult and inefficient via the classical methods. In addition, the dimensional dependency of the aquifer parameters as well as non-linear and non-convex fashion of inverse groundwater problems could make the stand-alone use of the metaheuristic algorithms inefficient in terms of computation time and effort, producing non-unique solutions. Therefore, a novel optimisation framework was established to interpret the pumping test data collected from an unconfined aquifer. The proposed approach works with four inputs which are based on the hybrid use of two non-dimensional physical and newly introduced two non-physical parameters. This study grasps the benefits of the simplicity of the traditional methods and the accuracy from Differential Evolution Algorithm (DE). The capability of the introduced scheme was broadly examined by several pumping test scenarios including hypothetical and the real field test datasets. A sensitivity analysis was also performed to understand the uncertainty associated with the estimated flow parameters. The results reveal that the proposed scheme powered by DE is able to achieve the outstanding estimation performance over the conventional methods and the implemented nature-inspired algorithms.