Scholarlink

<p>Seawater intrusion (SWI) poses a significant threat to coastal aquifers, leading to the deterioration of groundwater quality and undermining long-term water resource sustainability. Physical barriers, such as cutoff walls and subsurface dams, represent a viable mitigation strategy; however, their performance is highly dependent on site-specific design, which necessitates robust scenario-based simulation. In this study, a coupled Python-FEFLOW modeling framework was developed to dynamically assess the hydraulic and salinity responses of various physical barrier configurations, including both top-down and bottom-up implementations. The model quantifies key system responses, namely total dissolved salts (<i>S</i>) within the domain and seawater influx (<i>Qin</i>) across boundaries. Application of the model to the classical Henry problem, under uniform domain properties, revealed that cutoff walls achieved up to a 98% reduction in <i>S</i> and a 76.5% decrease in <i>Qin</i>. Subsurface dams were similarly effective, yielding reductions of approximately 92% in <i>S</i> and 81% in <i>Qin</i>. Notably, barrier placement proximal to the saline boundary led to stagnation zones and distortion of salinity contours, highlighting the importance of strategic positioning. To complement the simulation-based analysis, a hybrid machine learning approach, integrating Random Forest regression with SHAP analysis, was employed to perform sensitivity analysis and identify the most influential design parameters in a computationally efficient manner. This integrated modeling and machine learning framework offers a novel and scalable tool for optimizing physical barrier design This approach enabled a comprehensive and automated evaluation of physical barriers and their effectiveness in mitigating SWI. It also assessed the relative influence of physical barriers design parameters on SWI dynamics. The proposed methodology demonstrates significant potential for enhancing field-scale SWI mitigation strategies.</p> Graphical Abstract <p>This graphical abstract summarizes a novel, upscalable simulation–machine learning framework developed to optimize the design and placement of physical barriers for seawater intrusion (SWI) mitigation in coastal aquifers. The workflow initiates with the benchmark Henry problem<b>,</b> modeling density-driven flow to represent SWI dynamics. A custom Python-FEFLOW automation algorithm systematically varies barrier geometries—depth, width, and alignment—to simulate multiple design scenarios. These outputs feed into a comprehensive sensitivity analysis, enhanced by Random Forest Regression and SHAP (Shapley Additive Explanations) to quantify the influence of barrier parameters on key SWI indicators, including total dissolved salts (S) and saline inflow (Qin). The integrated approach enables the identification of optimal subsurface dam and cutoff wall configurations, as illustrated. The framework is fully upscalable and adaptable to any two-dimensional density-dependent flow model, offering a flexible, transparent, and data-driven methodology for early-stage design and decision-making in coastal groundwater management. </p> <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Hybrid Modeling Framework for Designing Subsurface Barriers to Mitigate Seawater Intrusion in Coastal Aquifers

  • 期刊论文

  • 发表日期：

    2025年7月29日

查看全文
  • Assaad Kassem,
  • Kassem El Cheikh Ali,
  • Ahmed Sefelnasr,
  • Mohsen Sherif

摘要

Seawater intrusion (SWI) poses a significant threat to coastal aquifers, leading to the deterioration of groundwater quality and undermining long-term water resource sustainability. Physical barriers, such as cutoff walls and subsurface dams, represent a viable mitigation strategy; however, their performance is highly dependent on site-specific design, which necessitates robust scenario-based simulation. In this study, a coupled Python-FEFLOW modeling framework was developed to dynamically assess the hydraulic and salinity responses of various physical barrier configurations, including both top-down and bottom-up implementations. The model quantifies key system responses, namely total dissolved salts (S) within the domain and seawater influx (Qin) across boundaries. Application of the model to the classical Henry problem, under uniform domain properties, revealed that cutoff walls achieved up to a 98% reduction in S and a 76.5% decrease in Qin. Subsurface dams were similarly effective, yielding reductions of approximately 92% in S and 81% in Qin. Notably, barrier placement proximal to the saline boundary led to stagnation zones and distortion of salinity contours, highlighting the importance of strategic positioning. To complement the simulation-based analysis, a hybrid machine learning approach, integrating Random Forest regression with SHAP analysis, was employed to perform sensitivity analysis and identify the most influential design parameters in a computationally efficient manner. This integrated modeling and machine learning framework offers a novel and scalable tool for optimizing physical barrier design This approach enabled a comprehensive and automated evaluation of physical barriers and their effectiveness in mitigating SWI. It also assessed the relative influence of physical barriers design parameters on SWI dynamics. The proposed methodology demonstrates significant potential for enhancing field-scale SWI mitigation strategies.

Graphical Abstract

This graphical abstract summarizes a novel, upscalable simulation–machine learning framework developed to optimize the design and placement of physical barriers for seawater intrusion (SWI) mitigation in coastal aquifers. The workflow initiates with the benchmark Henry problem, modeling density-driven flow to represent SWI dynamics. A custom Python-FEFLOW automation algorithm systematically varies barrier geometries—depth, width, and alignment—to simulate multiple design scenarios. These outputs feed into a comprehensive sensitivity analysis, enhanced by Random Forest Regression and SHAP (Shapley Additive Explanations) to quantify the influence of barrier parameters on key SWI indicators, including total dissolved salts (S) and saline inflow (Qin). The integrated approach enables the identification of optimal subsurface dam and cutoff wall configurations, as illustrated. The framework is fully upscalable and adaptable to any two-dimensional density-dependent flow model, offering a flexible, transparent, and data-driven methodology for early-stage design and decision-making in coastal groundwater management.