Contaminant Transport Modeling and Source Attribution With Attention‐Based Graph Neural Network

Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost....

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Veröffentlicht in:	Water resources research 2024-06, Vol.60 (6), p.n/a
Hauptverfasser:	Pang, Min, Du, Erhu, Zheng, Chunmiao
Format:	Artikel
Sprache:	eng
Schlagworte:	Anthropogenic factors Aquifer systems Aquifers Artificial neural networks Big Data Case studies Complexity Computational efficiency Computer applications Computing costs contaminant transport modeling Contaminants Contamination deep learning graph neural network Graph neural networks Groundwater groundwater contamination Groundwater management Groundwater pollution Groundwater protection Groundwater resources Human influences Learning algorithms Machine learning Modelling Monitoring Neural networks Observational learning Physics Pollution Pollution levels Pollution transport Robustness Short term memory source attribution Transport processes Water protection Water resources
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