Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses
Abstract Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-06613-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238663719813120 |
|---|---|
| author | Hossein Gholizadeh T. Prabhakar Clement Christopher T. Green Geoffrey R. Tick Alain M. Plattner Yong Zhang |
| author_facet | Hossein Gholizadeh T. Prabhakar Clement Christopher T. Green Geoffrey R. Tick Alain M. Plattner Yong Zhang |
| author_sort | Hossein Gholizadeh |
| collection | DOAJ |
| description | Abstract Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data. |
| format | Article |
| id | doaj-art-02fa063e90c842cdbb72546e7a67ab2d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-02fa063e90c842cdbb72546e7a67ab2d2025-08-20T04:01:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-06613-6Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stressesHossein Gholizadeh0T. Prabhakar Clement1Christopher T. Green2Geoffrey R. Tick3Alain M. Plattner4Yong Zhang5Department of Geological Sciences, University of AlabamaDepartment of Civil, Construction, and Environmental Engineering, University of AlabamaWater Resources Mission Area, U.S. Geological SurveyDepartment of Geological Sciences, University of AlabamaDepartment of Geological Sciences, University of AlabamaDepartment of Geological Sciences, University of AlabamaAbstract Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.https://doi.org/10.1038/s41598-025-06613-6Seawater intrusionPhysical modelMachine learningStorm surgePumping |
| spellingShingle | Hossein Gholizadeh T. Prabhakar Clement Christopher T. Green Geoffrey R. Tick Alain M. Plattner Yong Zhang Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses Scientific Reports Seawater intrusion Physical model Machine learning Storm surge Pumping |
| title | Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses |
| title_full | Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses |
| title_fullStr | Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses |
| title_full_unstemmed | Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses |
| title_short | Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses |
| title_sort | modeling seawater intrusion along the alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses |
| topic | Seawater intrusion Physical model Machine learning Storm surge Pumping |
| url | https://doi.org/10.1038/s41598-025-06613-6 |
| work_keys_str_mv | AT hosseingholizadeh modelingseawaterintrusionalongthealabamacoastlineusingphysicalandmachinelearningmodelstoevaluatetheeffectsofmultiscalenaturalandanthropogenicstresses AT tprabhakarclement modelingseawaterintrusionalongthealabamacoastlineusingphysicalandmachinelearningmodelstoevaluatetheeffectsofmultiscalenaturalandanthropogenicstresses AT christophertgreen modelingseawaterintrusionalongthealabamacoastlineusingphysicalandmachinelearningmodelstoevaluatetheeffectsofmultiscalenaturalandanthropogenicstresses AT geoffreyrtick modelingseawaterintrusionalongthealabamacoastlineusingphysicalandmachinelearningmodelstoevaluatetheeffectsofmultiscalenaturalandanthropogenicstresses AT alainmplattner modelingseawaterintrusionalongthealabamacoastlineusingphysicalandmachinelearningmodelstoevaluatetheeffectsofmultiscalenaturalandanthropogenicstresses AT yongzhang modelingseawaterintrusionalongthealabamacoastlineusingphysicalandmachinelearningmodelstoevaluatetheeffectsofmultiscalenaturalandanthropogenicstresses |