Cyclic Feedback Updating Approach and Uncertainty Analysis for the Source Identification of DNAPL-Contaminated Aquifers-Reference-Cited by-同舟云学术

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Cyclic Feedback Updating Approach and Uncertainty Analysis for the Source Identification of DNAPL-Contaminated Aquifers

Published:2021-02 Issue:2 Volume:147 Page:04020103
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Hou Zeyu¹,Lao Wangmei²,Wang Yu³,Lu Wenxi⁴

Affiliation:

1. Lecturer, College of Construction Engineering, Jilin Univ., No. 938 Ximinzhu St., Changchun 130000, China.

2. Professor, Dept. of Architecture and Chemical Engineering, Tangshan Polytechnic College, No. 25 Xinchengbohai St., Tangshan 063000, China.

3. Lecturer, College of New Energy and Environment, Jilin Univ., No. 2519 Jiefangdalu Rd., Changchun 130021, China (corresponding author).

4. Professor, College of New Energy and Environment, Jilin Univ., No. 2519 Jiefangdalu Rd., Changchun 130021, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001322

Reference44 articles.

1. A linked simulation–optimization model for solving the unknown groundwater pollution source identification problems

2. A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems

3. Near real-time atmospheric contamination source identification by an optimization-based inverse method

4. Development of an Improved Pollution Source Identification Model Using Numerical and ANN Based Simulation-Optimization Model

5. Linear homotopy method for computing generalized tensor eigenpairs

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1. 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

2. A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity;Water Resources Research;2024-03-28

3. A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification;Journal of Hydrology;2023-01

4. Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning;Environmental Science and Pollution Research;2022-12-30

5. Study on source identification and source-sink relationship of LNAPLs pollution in groundwater by the adaptive cyclic improved iterative process and Monte Carlo stochastic simulation;Journal of Hydrology;2022-09

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