Identifying groundwater contamination sources based on the hybrid grey wolf gradient algorithm and deep belief neural network-Reference-Cited by-同舟云学术

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Identifying groundwater contamination sources based on the hybrid grey wolf gradient algorithm and deep belief neural network

Published:2022-12-20 Issue:5 Volume:37 Page:1697-1715
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Li Jiuhui,Wu Zhengfang,He Hongshi,Lu Wenxi

Funder

the National Nature Science Foundation of China

the Fundamental Research Funds for the Central Universities

the National Key R&D Program of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-022-02360-6.pdf

Reference66 articles.

1. Ajiz MA, Jennings A (1984) A robust incomplete Choleski-conjugate gradient algorithm. Int J Numer Methods Eng. https://doi.org/10.1002/nme.1620200511

2. Alaa F, Abdel-Raouf A (2006) Estimating the parameters of software reliability growth models using the grey wolf optimization algorithm. Int J Adv Comput Sci Appl 7(4):25. https://doi.org/10.14569/IJACSA.2016.070465

3. Andrei N (2006) An acceleration of gradient descent algorithm with backtracking for unconstrained optimization. Numer Algorithms 42(1):63–73. https://doi.org/10.1007/s11075-006-9023-9

4. Asher MJ, Croke BFW, Jakeman AJ et al (2015) A review of surrogate models and their application to groundwater modeling. Water Resour Res 51(8):5957–5973. https://doi.org/10.1002/2015WR016967

5. Atmadja J, Bagtzoglou AC (2001) State of the art report on mathematical methods for groundwater pollution source identification. Environ Forensics 2(3):205–214. https://doi.org/10.1006/enfo.2001.0055

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identification of Groundwater Contamination Sources Based on a Deep Belief Neural Network;Water;2024-08-29

2. Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy;Journal of Hydrology;2024-06

3. Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter;Journal of Hydrology;2023-10

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