PMID- 38522380 OWN - NLM STAT- MEDLINE DCOM- 20240428 LR - 20240429 IS - 1873-6009 (Electronic) IS - 0169-7722 (Linking) VI - 263 DP - 2024 Apr TI - Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion. PG - 104337 LID - S0169-7722(24)00041-X [pii] LID - 10.1016/j.jconhyd.2024.104337 [doi] AB - 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. CI - Copyright (c) 2023. Published by Elsevier B.V. FAU - Konstantinou, Charalampos AU - Konstantinou C AD - Department of Civil and Environmental Engineering, University of Cyprus, Nicosia, Cyprus. Electronic address: ckonst06@ucy.ac.cy. FAU - Wang, Yuze AU - Wang Y AD - Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China. Electronic address: wangyz@sustech.edu.cn. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20240320 PL - Netherlands TA - J Contam Hydrol JT - Journal of contaminant hydrology JID - 8805644 RN - 0 (Carbonates) SB - IM MH - *Seawater/chemistry/microbiology MH - *Machine Learning MH - *Groundwater/chemistry MH - *Carbonates/chemistry/metabolism MH - Chemical Precipitation MH - Water Movements OTO - NOTNLM OT - Biochemical cut-off wall OT - Hydraulic conductivity OT - Machine learning OT - Microbially induced carbonate precipitation OT - Seawater intrusion OT - Statistical analysis COIS- Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Charalampos Konstantinou reports financial support was provided by Cyprus Research Promotion Foundation. Yuze Wang reports financial support was provided by Science and Technology Innovation Committee of Shenzhen. Yuze Wang reports financial support was provided by Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. EDAT- 2024/03/25 00:42 MHDA- 2024/04/29 00:58 CRDT- 2024/03/24 19:12 PHST- 2023/12/16 00:00 [received] PHST- 2024/03/15 00:00 [revised] PHST- 2024/03/17 00:00 [accepted] PHST- 2024/04/29 00:58 [medline] PHST- 2024/03/25 00:42 [pubmed] PHST- 2024/03/24 19:12 [entrez] AID - S0169-7722(24)00041-X [pii] AID - 10.1016/j.jconhyd.2024.104337 [doi] PST - ppublish SO - J Contam Hydrol. 2024 Apr;263:104337. doi: 10.1016/j.jconhyd.2024.104337. Epub 2024 Mar 20.