Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion

Konstantinou, Charalampos1; Wang, Yuze2,3

2024-04-01

10.1016/j.jconhyd.2024.104337

JOURNAL OF CONTAMINANT HYDROLOGY   影响因子和分区

0169-7722

1873-6009

Seawater intrusion in coastal aquifers is a significant problem that can be addressed through the construction of subsurface dams or physical cut-off barriers. An alternative method is the use of microbially induced carbonate precipitation (MICP) to reduce the hydraulic conductivity of the porous medium and create a physical barrier. However, the effectiveness of this method depends on various factors, and the scientific literature presents conflicting results, making it challenging to generalise the findings. To overcome this challenge, a statistical and machine learning (ML) approach is employed to infer the causes for the reduction in hydraulic conductivity and identify the optimum MICP parameters for preventing seawater intrusion. The study involves data curation, exploratory analysis, and the development of various models to fit the input data (k-Nearest Neighbours - kNN, Support Vector Regression - SVR, Random Forests - RF, Gradient Boosting - XgBoost, Linear model with interaction terms, Ensemble learning algorithms with weighted averages - EnL-WA and stacking - EnL-Stack). The models performed reasonably well in the region where permeability reduction is sensitive to carbonate increase capturing the permeability reduction profile with respect to cementation level while demonstrating that they can be used in initial assessments of the specific conditions (e.g., soil properties). The best performing algorithms were the EnL-Stack and RF followed by XgBoost and SVR. The MICP method is effective in reducing hydraulic conductivity provided that the various biochemical parameters are optimised. Critical biochemical parameters for successful MICP formulations are the bacterial optical density, the urease activity, calcium chloride concentration and flow rate as well as the interaction terms across the properties of the porous media and the biochemical parameters. The models were used to identify the optimum MICP formulation for various porous media properties and the maximum permeability reduction profiles across cementation levels have been derived.

Seawater intrusion Biochemical cut-off wall Microbially induced carbonate precipitation Machine learning Statistical analysis Hydraulic conductivity

SCI ; EI

英语

通讯

Republic of Cyprus through the Research Promotion Foundation (RPF) (Cyprus RPF)[EXCELLENCE/0421/0456] ; Science and Technology Innovation Committee of Shenzhen[JCYJ20210324103812033] ; Natural Science Foundation of China[52171262]

Environmental Sciences & Ecology ; Geology ; Water Resources

Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources

WOS:001220274000001

ELSEVIER

ENVIRONMENT/ECOLOGY

Web of Science

1.Univ Cyprus, Dept Civil & Environm Engn, Nicosia, Cyprus
2.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen, Peoples R China
3.Southern Marine Sci & Engn Guangdong Lab, Guangzhou, Peoples R China

Konstantinou, Charalampos,Wang, Yuze. Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion[J]. JOURNAL OF CONTAMINANT HYDROLOGY,2024,263.

Konstantinou, Charalampos,&Wang, Yuze.(2024).Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion.JOURNAL OF CONTAMINANT HYDROLOGY,263.

Konstantinou, Charalampos,et al."Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion".JOURNAL OF CONTAMINANT HYDROLOGY 263(2024).