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Torres-Martínez JA, Mahlknecht J, Kumar M, Loge FJ, Kaown D. Advancing groundwater quality predictions: Machine learning challenges and solutions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:174973. [PMID: 39053524 DOI: 10.1016/j.scitotenv.2024.174973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/22/2024] [Accepted: 07/20/2024] [Indexed: 07/27/2024]
Abstract
Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.
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Affiliation(s)
- Juan Antonio Torres-Martínez
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterrey, Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico
| | - Jürgen Mahlknecht
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterrey, Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico.
| | - Manish Kumar
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Campus Monterrey, Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico; School of Engineering, University of Petroleum & Energy Studies, Dehradun, Uttarakhand 248007, India
| | - Frank J Loge
- Department of Civil and Environmental Engineering, University of California Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Dugin Kaown
- School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Republic of Korea
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Guo L, Xu X, Niu C, Wang Q, Park J, Zhou L, Lei H, Wang X, Yuan X. Machine learning-based prediction and experimental validation of heavy metal adsorption capacity of bentonite. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171986. [PMID: 38552979 DOI: 10.1016/j.scitotenv.2024.171986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/23/2024] [Accepted: 03/24/2024] [Indexed: 04/01/2024]
Abstract
As a natural adsorbent material, bentonite is widely used in the field of heavy metal adsorption. The heavy metal adsorption capacity of bentonite varies significantly in studies due to the differences in the properties of bentonite, solution, and heavy metal. To achieve accurate predictions of bentonite's heavy metal adsorption capacity, this study employed six machine learning (ML) regression algorithms to investigate the adsorption characteristics of bentonite. Finally, an eXtreme Gradient Boosting Regression (XGB) model with outstanding predictive performance was constructed. Explanation analysis of the XGB model further reveal the importance and influence manner of each input feature in predicting the heavy metal adsorption capacity of bentonite. The feature categories influencing heavy metal adsorption capacity were ranked in order of importance as adsorption conditions > bentonite properties > heavy metal properties. Furthermore, a web-based graphical user interface (GUI) software was developed, facilitating researchers and engineers to conveniently use the XGB model for predicting the heavy metal adsorption capacity of bentonite. This study provides new insights into the adsorption behaviors of bentonite for heavy metals, offering guidance and support for enhancing its application efficiency and addressing heavy metal pollution remediation.
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Affiliation(s)
- Lisheng Guo
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Xin Xu
- College of Construction Engineering, Jilin University, Changchun 130026, China.
| | - Cencen Niu
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Qing Wang
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Junboum Park
- Department of Civil and Environment Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Lu Zhou
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Haomin Lei
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Xinhai Wang
- College of Construction Engineering, Jilin University, Changchun 130026, China
| | - Xiaoqing Yuan
- College of Construction Engineering, Jilin University, Changchun 130026, China
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Liu Z, Yang Q, Zhu P, Liu Y, Tong X, Cao T, Tomson MB, Alvarez PJJ, Zhang T, Chen W. Cr(VI) Reduction and Sequestration by FeS Nanoparticles Formed in situ as Aquifer Material Coating to Create a Regenerable Reactive Zone. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7186-7195. [PMID: 38598770 DOI: 10.1021/acs.est.3c10637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Remediation of large and dilute plumes of groundwater contaminated by oxidized pollutants such as chromate is a common and difficult challenge. Herein, we show that in situ formation of FeS nanoparticles (using dissolved Fe(II), S(-II), and natural organic matter as a nucleating template) results in uniform coating of aquifer material to create a regenerable reactive zone that mitigates Cr(VI) migration. Flow-through columns packed with quartz sand are amended first with an Fe2+ solution and then with a HS- solution to form a nano-FeS coating on the sand, which does not hinder permeability. This nano-FeS coating effectively reduces and immobilizes Cr(VI), forming Fe(III)-Cr(III) coprecipitates with negligible detachment from the sand grains. Preconditioning the sand with humic or fulvic acid (used as model natural organic matter (NOM)) further enhances Cr(VI) sequestration, as NOM provides additional binding sites of Fe2+ and mediates both nucleation and growth of FeS nanoparticles, as verified with spectroscopic and microscopic evidence. Reactivity can be easily replenished by repeating the procedures used to form the reactive coating. These findings demonstrate that such enhancement of attenuation capacity can be an effective option to mitigate Cr(VI) plume migration and exposure, particularly when tackling contaminant rebound post source remediation.
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Affiliation(s)
- Zhenhai Liu
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
| | - Qihong Yang
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
| | - Panpan Zhu
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
| | - Yaqi Liu
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
| | - Xin Tong
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
| | - Tianchi Cao
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
| | - Mason B Tomson
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, Texas 77005, United States
| | - Pedro J J Alvarez
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, Texas 77005, United States
| | - Tong Zhang
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
| | - Wei Chen
- College of Environmental Science and Engineering, Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Rd., Tianjin 300350, China
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Zhang S, Wang S, Zhao J, Zhu L. Predicting thermal desorption efficiency of PAHs in contaminated sites based on an optimized machine learning approach. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 346:123667. [PMID: 38428795 DOI: 10.1016/j.envpol.2024.123667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024]
Abstract
Thermal desorption (TD) remediation of polycyclic aromatic hydrocarbon (PAH)-contaminated sites is known for its high energy consumption and cost implications. The key to solving this issue lies in analyzing the PAHs desorption process, defining remediation endpoints, and developing prediction models to prevent excessive remediation. Establishing an accurate prediction model for remediation efficiency, which involves a systematic consideration of soil properties, TD parameters, and PAH characteristics, poses a significant challenge. This study employed a machine learning approach for predicting the remediation efficiency based on batch experiment results. The results revealed that the extreme gradient boosting (XGB) model yielded the most accurate predictions (R2 = 0.9832). The importance of features in the prediction process was quantified. A model optimization scheme was proposed, which involved integrating features based on their relevance, importance, and partial dependence. This integration not only reduced the number of input features but also enhanced prediction accuracy (R2 = 0.9867) without eliminating any features. The optimized XGB model was validated using soils from sites, demonstrating a prediction error of less than 30%. The optimized XGB model aids in identifying the most optimal conditions for thermal desorption to maximize the remediation efficiency of PAH-contaminated sites under relative cost and energy-saving conditions.
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Affiliation(s)
- Shuai Zhang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang, 310058, China
| | - Shuyuan Wang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang, 310058, China
| | - Jiating Zhao
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang, 310058, China
| | - Lizhong Zhu
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang, 310058, China.
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Rad M, Abtahi A, Berndtsson R, McKnight US, Aminifar A. Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 345:123449. [PMID: 38278404 DOI: 10.1016/j.envpol.2024.123449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 01/28/2024]
Abstract
Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications. We used ML to enhance the understanding of the dynamics of PCP transport properties in the subsurface, and to determine key hydrochemical and hydrogeological drivers affecting its transport and fate. We demonstrate how this complementary knowledge, provided by data-driven methods, may enable a more targeted planning of monitoring and remediation at two highly contaminated Swedish groundwater sites, where the method was validated. We evaluated 6 interpretable ML methods, 3 linear regressors and 3 non-linear (i.e., tree-based) regressors, to predict PCP concentration in the groundwater. The modeling results indicate that simple linear ML models were found to be useful in the prediction of observations for datasets without any missing values, while tree-based regressors were more suitable for datasets containing missing values. Considering that missing values are common in datasets collected during contaminated site investigations, this could be of significant importance for contaminated site planners and managers, ultimately reducing site investigation and monitoring costs. Furthermore, we interpreted the proposed models using the SHAP (SHapley Additive exPlanations) approach to decipher the importance of different drivers in the prediction and simulation of critical hydrogeochemical variables. Among these, sum of chlorophenols is of highest significance in the analyses. Setting that aside from the model, tetra chlorophenols, dissolved organic carbon, and conductivity found to be of highest importance. Accordingly, ML methods could potentially be used to improve the understanding of groundwater contamination transport dynamics, filling gaps in knowledge that remain when using more sophisticated deterministic modeling approaches.
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Affiliation(s)
- Mehran Rad
- Department of Agriculture and Food, Research Institutes of Sweden (RISE), Box 5401, SE-402 29, Göteborg, Sweden; Division of Water Resources Engineering, Department of Building and Environmental Technology, Lund University, Box 118, SE-221 00, Lund, Sweden.
| | - Azra Abtahi
- Department of Electrical and Information Technology, Lund University, Box 118, SE-221 00 Lund, Sweden
| | - Ronny Berndtsson
- Division of Water Resources Engineering, Department of Building and Environmental Technology, Lund University, Box 118, SE-221 00, Lund, Sweden; Centre for Advanced Middle Eastern Studies, Lund University, Box 201, SE-221 00, Lund, Sweden
| | - Ursula S McKnight
- Swedish Meteorological and Hydrological Institute, SE-601 76, Norrköping, Sweden
| | - Amir Aminifar
- Department of Electrical and Information Technology, Lund University, Box 118, SE-221 00 Lund, Sweden
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Yang L, Zhang T, Gao Y, Li D, Cui R, Gu C, Wang L, Sun H. Quantitative identification of the co-exposure effects of e-waste pollutants on human oxidative stress by explainable machine learning. JOURNAL OF HAZARDOUS MATERIALS 2024; 466:133560. [PMID: 38246054 DOI: 10.1016/j.jhazmat.2024.133560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/04/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Global electronic waste (e-waste) generation continues to grow. The various pollutants released during precarious e-waste disposal activities can contribute to human oxidative stress. This study encompassed 129 individuals residing near e-waste dismantling sites in China, with elevated urinary concentrations of e-waste-related pollutants including heavy metals, polycyclic aromatic hydrocarbons (PAHs), organophosphorus flame retardants (OPFRs), bisphenols (BPs), and phthalate esters (PAEs). Utilizing an explainable machine learning framework, the study quantified the co-exposure effects of these pollutants, finding that approximately 23% and 18% of the variance in oxidative DNA damage and lipid peroxidation, respectively, was attributable to these substances. Heavy metals emerged as the most critical factor in inducing oxidative stress, followed by PAHs and PAEs for oxidative DNA damage, and BPs, OPFRs, and PAEs for lipid peroxidation. The interactions between different pollutant classes were found to be weak, attributable to their disparate biological pathways. In contrast, the interactions among congeneric pollutants were strong, stemming from their shared pathways and resultant synergistic or additive effects on oxidative stress. An intelligent analysis system for e-waste pollutants was also developed, which enables more efficient processing of large-scale and dynamic datasets in evolving environments. This study offered an enticing peek into the intricacies of co-exposure effect of e-waste pollutants.
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Affiliation(s)
- Luhan Yang
- School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
| | - Tao Zhang
- School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China.
| | - Yanxia Gao
- School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
| | - Dairui Li
- School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
| | - Rui Cui
- School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
| | - Cheng Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Lei Wang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Hongwen Sun
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
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Janga JK, Reddy KR, Raviteja KVNS. Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review. CHEMOSPHERE 2023; 345:140476. [PMID: 37866497 DOI: 10.1016/j.chemosphere.2023.140476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/24/2023]
Abstract
The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.
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Affiliation(s)
- Jagadeesh Kumar Janga
- University of Illinois Chicago, Department of Civil, Materials, and Environmental Engineering, 842 West Taylor Street, Chicago, IL 60607, USA.
| | - Krishna R Reddy
- University of Illinois Chicago, Department of Civil, Materials, and Environmental Engineering, 842 West Taylor Street, Chicago, IL 60607, USA.
| | - K V N S Raviteja
- SRM University AP, Department of Civil Engineering, Guntur, Andhra Pradesh 522503, India.
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Tan Y, Zhang P, Chen J, Shamet R, Hyun Nam B, Pu H. Predicting the hydraulic conductivity of compacted soil barriers in landfills using machine learning techniques. WASTE MANAGEMENT (NEW YORK, N.Y.) 2023; 157:357-366. [PMID: 36630884 DOI: 10.1016/j.wasman.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/18/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
Machine learning models (MLMs) were developed to predict saturated hydraulic conductivity of compacted soil barriers and help to identify appropriate soils for the construction of landfill liners and covers. Data from hydraulic conductivity tests on compacted soil barriers were collected from the literature and compiled into a database for MLM construction. The database contains 329 records of hydraulic conductivity tests associated with 12 selected impact factors covering physical properties, compaction efforts, and hydration and mineralogy behaviors of compacted soil barriers. Three machine learning algorithms (random forest, gradient boosting decision tree, and neural network) were used to develop MLMs, and a statistical technique (multiple linear regression) was used to compare the precision of predictions with the MLMs. Results from this study showed that the random forest model provided the best prediction of the hydraulic conductivity of compacted soil barriers, with 100% of predicted hydraulic conductivity within 100-time differences to measured hydraulic conductivity and 93% within 10-time differences. Feature importance analysis showed that void ratio after compaction, fines content, specific gravity, degree of saturation after compaction, and plasticity index of soils are the top-five factors (in descending order) that influence the hydraulic conductivity of compacted soil barriers and are recommended for a precise prediction. Three predictive MLMs were created for industries as simple tools to screen the soils prior to the construction of compacted soil barriers in landfill liners and covers.
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Affiliation(s)
- Yu Tan
- Civil and Environmental Engineering, Univ. of Wisconsin, Madison, WI 53706, USA
| | - Poyu Zhang
- Civil, Environ., & Const. Engineering, Univ. of Central Florida, Orlando, FL 32816, USA
| | - Jiannan Chen
- Civil, Environ., & Const. Engineering, Univ. of Central Florida, Orlando, FL 32816, USA.
| | - Ryan Shamet
- Civil Engineering, University of North Florida, Jacksonville, FL 32224, USA
| | - Boo Hyun Nam
- Department of Civil Engineering, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Hefu Pu
- Civil and Hydraulic Engr., Huazhong Univ. of Science and Technology, Wuhan 430074, China
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