- -

Simultaneous identification of a non-point contaminant source with Gaussian spatially distributed release and heterogeneous hydraulic conductivity in an aquifer using the LES-MDA method

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Simultaneous identification of a non-point contaminant source with Gaussian spatially distributed release and heterogeneous hydraulic conductivity in an aquifer using the LES-MDA method

Mostrar el registro completo del ítem

Zhang, W.; Xu, T.; Chen, Z.; Gómez-Hernández, JJ.; Lu, C.; Yang, J.; Ye, Y.... (2024). Simultaneous identification of a non-point contaminant source with Gaussian spatially distributed release and heterogeneous hydraulic conductivity in an aquifer using the LES-MDA method. Journal of Hydrology. 630. https://doi.org/10.1016/j.jhydrol.2024.130745

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/206289

Ficheros en el ítem

Metadatos del ítem

Título: Simultaneous identification of a non-point contaminant source with Gaussian spatially distributed release and heterogeneous hydraulic conductivity in an aquifer using the LES-MDA method
Autor: Zhang, Wenjun Xu, Teng Chen, Zi Gómez-Hernández, J. Jaime Lu, Chunhui Yang, Jie Ye, Yu Miao Jing
Entidad UPV: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports
Fecha difusión:
Fecha de fin de embargo: 2026-01-31
Resumen:
[EN] Space -temporal distribution of the contaminant plumes and aquifer properties is critical for groundwater management. However, most previous studies have focused on point source identification, barely exploring the ...[+]
Palabras clave: Non-point contaminant source identification , Data assimilation , Ensemble smoother with multiple data , Assimilation , Localization
Derechos de uso: Embargado
Fuente:
Journal of Hydrology. (issn: 0022-1694 )
DOI: 10.1016/j.jhydrol.2024.130745
Editorial:
Elsevier
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109131RB-I00/ES/APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES/
...[+]
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109131RB-I00/ES/APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES/
info:eu-repo/grantAgreement/AEI//PRE2020-093145//APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES/
info:eu-repo/grantAgreement/NSFC//42377046/
info:eu-repo/grantAgreement/NSFC//51879088/
info:eu-repo/grantAgreement/Fundamental Research Funds for the Central Universities//B200204002/
info:eu-repo/grantAgreement/Natural Science Foundation of Jiangsu Province//BK 20190023/
info:eu-repo/grantAgreement/NKRDPC//2021YFC3200500/
[-]
Agradecimientos:
Financial support to carry out this work was received from the financial support from the National Key Research and Development Project (2021YFC3200500) , and the National Natural Science Foundation of China (Grant No. ...[+]
Tipo: Artículo

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem