Breaking the mold of simulation-optimization: Direct forward machine learning methods for groundwater contaminant source identification

•We proposed two GCSI methods that can make estimations without employing any optimization algorithm, thus improving estimation efficiency and reducing implementation cost.•The first Direct Forward Machine Learning (DFML) method can accurately estimate eight parameters related to contaminant source...

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Veröffentlicht in:	Journal of hydrology (Amsterdam) Jg. 642; S. 131759
Hauptverfasser:	Wang, Chaoqi, Dou, Zhi, Zhu, Yan, Yang, Ze, Zou, Zhihan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2024
Schlagworte:	algorithms aquifers Artificial Neural Network Contaminant source identification groundwater contamination Groundwater modeling Machine Learning One-hot encoding probability distribution One-hot encoding Contaminant source identification Artificial Neural Network Groundwater modeling Machine Learning
ISSN:	0022-1694
Online-Zugang:	Volltext
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