A methodological review of AI/ML and evolutionary computation in hydrology and hydraulics: from ANN/SVM to deep learning, symbolic regression, and physics-informed models.
- DOI
- 10.2166/wst.2026.304
- Published
- 2026 Jul
- Container
- Water science and technology : a journal of the International Association on Water Pollution Research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.2166/wst.2026.304,
title = {A methodological review of AI/ML and evolutionary computation in hydrology and hydraulics: from ANN/SVM to deep learning, symbolic regression, and physics-informed models.},
author = {Arganis-Juárez ML and Preciado M},
year = {2026},
journal = {Water science and technology : a journal of the International Association on Water Pollution Research},
doi = {10.2166/wst.2026.304},
url = {https://doi.org/10.2166/wst.2026.304}
}RIS
TY - JOUR TI - A methodological review of AI/ML and evolutionary computation in hydrology and hydraulics: from ANN/SVM to deep learning, symbolic regression, and physics-informed models. AU - Arganis-Juárez ML AU - Preciado M PY - 2026 JO - Water science and technology : a journal of the International Association on Water Pollution Research DO - 10.2166/wst.2026.304 UR - https://doi.org/10.2166/wst.2026.304 ER -
APA
ML, A., & M, P. (2026). A methodological review of AI/ML and evolutionary computation in hydrology and hydraulics: from ANN/SVM to deep learning, symbolic regression, and physics-informed models.. Water science and technology : a journal of the International Association on Water Pollution Research. https://doi.org/10.2166/wst.2026.304
Source records
- pubmed · retrieved 2026-09-25T17:00:51.156Z