Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems
Abstrak
In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.
Topik & Kata Kunci
Penulis (7)
George Kopsiaftis
Maria Kaselimi
Eftychios Protopapadakis
Athanasios Voulodimos
Anastasios Doulamis
Nikolaos Doulamis
Aristotelis Mantoglou
Format Sitasi
Akses Cepat
- Tahun Terbit
- 2023
- Sumber Database
- DOAJ
- DOI
- 10.3389/frwa.2023.1195029
- Akses
- Open Access ✓