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Deep Learning-Based Methods for Groundwater Contamination Identification
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Deep Learning-Based Methods for Groundwater Contamination Identification

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 3056

Special Issue Editors

Dr. Simin Jiang

E-Mail Website
Guest Editor
Department of Hydraulic Engineering, Tongji University, Shanghai 200092, China
Interests: groundwater simulation; inverse problem; contaminant hydrogeology; intelligent simulation of water environments; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Zhenbo Chang

E-Mail Website
Guest Editor Assistant
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, China
Interests: simulation modeling; groundwater; algorithms; water quality; parameter estimation; sensitivity analysis

Special Issue Information

Dear Colleagues,

Groundwater is an essential resource for human economic production and livelihood, playing an irreplaceable role in maintaining socio-economic development and ecological balance. However, the increasingly severe problem of groundwater pollution poses a significant threat to the security of groundwater resources. In recent years, with rapid developments in technology, the applications of deep learning in the field of environmental science have been receiving increasing attention. Therefore, how to efficiently identify, predict, and assess groundwater pollution using deep learning methods is currently a hot topic of research. We are delighted to invite you to contribute your innovative findings on "Deep Learning-Based Methods for Groundwater Contamination Identification" to make a contribution to this theme. These papers can include, but are not limited to, the following topics:

(1) Identification of groundwater pollution sources based on deep learning;

(2) Deep learning models for predicting groundwater pollution;

(3) Applications of deep learning methods in the assessment of groundwater quality;

(4) Applications of deep learning methods in the control and remediation of groundwater pollution.

Dr. Simin Jiang
Guest Editor

Dr. Zhenbo Chang
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • artificial neural network
  • groundwater pollution
  • contaminant source identification
  • groundwater quality assessment
  • groundwater pollution control and remediation

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Published Papers (4 papers)

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Research

15 pages, 6534 KiB  
Article
Groundwater Pollution Source and Aquifer Parameter Estimation Based on a Stacked Autoencoder Substitute
by Han Wang, Jinping Zhang, Hang Li, Guanghua Li, Jiayuan Guo and Wenxi Lu
Water 2024, 16(18), 2564; https://doi.org/10.3390/w16182564 - 10 Sep 2024
Viewed by 372
Abstract
A concurrent heuristic search iterative process (CHSIP) is used for estimating groundwater pollution sources and aquifer parameters in this work. Frequent calls to carry out a numerical simulation of groundwater pollution have generated a huge calculated load during the CHSIP. Therefore, a valid [...] Read more.
A concurrent heuristic search iterative process (CHSIP) is used for estimating groundwater pollution sources and aquifer parameters in this work. Frequent calls to carry out a numerical simulation of groundwater pollution have generated a huge calculated load during the CHSIP. Therefore, a valid means to mitigate this is building a substitute to emulate the numerical simulation at a low calculated load. However, there is a complicated nonlinear correlativity between the import and export of the numerical simulation on account of the large quantity of variables. This leads to a poor approach accuracy of the substitute compared to the simulation when using shallow learning methods. Therefore, we first built a stacked autoencoder substitute, using the deep learning method, to boost the approach accuracy of the substitute compared to the numerical simulation. In total, 400 training samples and 100 testing samples for the substitute were collected by employing the Latin hypercube sampling method and running the numerical simulator. The CHSIP was then employed for estimating the groundwater pollution sources and aquifer parameters, and the estimated outcome was obtained when the CHSIP was terminated. The data analysis, including interval estimation and point estimation, was implemented on the MATLAB platform. A relevant hypothetical case is set to verify our approaches, which shows that the CHSIP is helpful for estimating the groundwater pollution source and aquifer parameters and that the stacked autoencoder method can effectively boost the approach precision of the substitute for the simulator. Full article
(This article belongs to the Special Issue Deep Learning-Based Methods for Groundwater Contamination Identification)
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23 pages, 7922 KiB  
Article
Groundwater LNAPL Contamination Source Identification Based on Stacking Ensemble Surrogate Model
by Yukun Bai, Wenxi Lu, Zibo Wang and Yaning Xu
Water 2024, 16(16), 2274; https://doi.org/10.3390/w16162274 - 12 Aug 2024
Viewed by 815
Abstract
Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is [...] Read more.
Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is an effective way to overcome this challenge. However, how to obtain high-quality samples for training the surrogate model and which method should be used to develop the surrogate model with higher accuracy remain important questions to explore. To this end, this paper innovatively adopted the quasi-Monte Carlo (QMC) method to sample from the prior space of unknown variables. Then, this paper established a variety of individual machine learning surrogate models, respectively, and screened three with higher training accuracy among them as the base-learning models (BLMs). The Stacking ensemble framework was utilized to integrate the three BLMs to establish the ensemble surrogate model for the groundwater LNAPL multiphase flow numerical simulation model. Finally, a hypothetical case of groundwater LNAPL contamination was designed. After evaluating the accuracy of the Stacking ensemble surrogate model, the differential evolution Markov chain (DE-MC) algorithm was applied to jointly identify information on groundwater LNAPL contamination source and key hydrogeological parameters. The results of this study demonstrated the following: (1) Employing the QMC method to sample from the prior space resulted in more uniformly distributed and representative samples, which improved the quality of the training data. (2) The developed Stacking ensemble surrogate model had a higher accuracy than any individual surrogate model, with an average R2 of 0.995, and reduced the computational burden by 99.56% compared to the inversion process based on the simulation model. (3) The application of the DE-MC algorithm effectively solved the GLCSI problem, and the mean relative error of the identification results of unknown variables was less than 5%. Full article
(This article belongs to the Special Issue Deep Learning-Based Methods for Groundwater Contamination Identification)
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16 pages, 2386 KiB  
Article
Informed Search Strategy for Synchronous Recognition of Groundwater Pollution Sources and Aquifer Parameters Based on an Improved DCN Substitute
by Guanghua Li, Han Wang, Jiayuan Guo, Jinping Zhang and Wenxi Lu
Water 2024, 16(15), 2143; https://doi.org/10.3390/w16152143 - 29 Jul 2024
Viewed by 656
Abstract
An informed search strategy based on random statistical analysis was developed for synchronous recognition of groundwater pollution source information and aquifer parameters. An informed search iterative course (ISIC) was accordingly designed, and each iteration included the determination of attempt point and state transition. [...] Read more.
An informed search strategy based on random statistical analysis was developed for synchronous recognition of groundwater pollution source information and aquifer parameters. An informed search iterative course (ISIC) was accordingly designed, and each iteration included the determination of attempt point and state transition. In this paper, two improvement techniques were first adopted for choosing attempt points and judging state transition in ISIC to improve search efficiency and precision. The first improvement was that the variable radius free search method was applied to choosing the attempt point, and the size of the search radius was constantly adjusted in ISIC, taking the search ergodicity and efficiency into account. The second improvement technique was a Tsallis formula used for state transition judgment, and the controlled factor in the Tsallis formula was regulated continuously so that the search could consider ergodicity and efficiency simultaneously. Furthermore, frequent calls to the groundwater pollution numerical simulator to calculate the likelihood have inflicted a huge computational burden during ISIC. An effective way is to construct a substitute for emulating the simulator with a low calculating load. However, the mapping relation between the import and export of the numerical simulator was complex and had many variables. The precision of the substitute based on shallow learning is low sometimes. Therefore, we adopted the deep learning method and built an improved deep confidence network (DCN) substitute to emulate the highly nonlinear simulator. Finally, the synchronous recognition results for groundwater pollution source information and aquifer parameters were gained when ISIC ceased. The above-mentioned methods were verified in a case involving groundwater pollution. The consequence indicated that the ISIC with an improved DCN substitute can synchronously recognize groundwater pollution source information and aquifer parameters with a high degree of precision and efficiency. Full article
(This article belongs to the Special Issue Deep Learning-Based Methods for Groundwater Contamination Identification)
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20 pages, 6803 KiB  
Article
Groundwater Contamination Source Recognition Based on a Two-Stage Inversion Framework with a Deep Learning Surrogate
by Zibo Wang and Wenxi Lu
Water 2024, 16(13), 1907; https://doi.org/10.3390/w16131907 - 3 Jul 2024
Cited by 1 | Viewed by 841
Abstract
Groundwater contamination source recognition is an important prerequisite for subsequent remediation efforts. To overcome the limitations of single inversion methods, this study proposed a two-stage inversion framework by integrating two primary inversion approaches—simulation-optimization and simulation-data assimilation—thereby enhancing inversion accuracy. In the first stage, [...] Read more.
Groundwater contamination source recognition is an important prerequisite for subsequent remediation efforts. To overcome the limitations of single inversion methods, this study proposed a two-stage inversion framework by integrating two primary inversion approaches—simulation-optimization and simulation-data assimilation—thereby enhancing inversion accuracy. In the first stage, the ensemble smoother with multiple data assimilation method (a type of simulation-data assimilation) conducted a global broad search to provide better initial values and ranges for the second stage. In the subsequent stage, a collective decision optimization algorithm (a type of simulation-optimization) was used for a refined deep search, further enhancing the final inversion accuracy. Additionally, a deep learning method, the multilayer perceptron, was utilized to establish a surrogate of the simulation model, reducing computational costs. These theories and methods were applied and validated in a hypothetical scenario for the synchronous identification of the contamination source and boundary conditions. The results demonstrated that the proposed two-stage inversion framework significantly improved search accuracy compared to single inversion methods, with a mean relative error and mean absolute error of just 4.95% and 0.1756, respectively. Moreover, the multilayer perceptron surrogate model offered greater approximation accuracy to the simulation model than the traditional shallow learning surrogate model. Specifically, the coefficient of determination, mean relative error, mean absolute error, and root mean square error were 0.9860, 9.72%, 0.1727, and 0.47, respectively, highlighting its significant advantages. The findings of this study can provide more reliable technical support for practical case applications and improve subsequent remediation efficiency. Full article
(This article belongs to the Special Issue Deep Learning-Based Methods for Groundwater Contamination Identification)
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