用深度学习和元启发算法对沿海含水层脆弱性评估进行比较研究
Mojgan Bordbar1, Essam Heggy2,3, Changhyun Jun4
1Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli", Via Vivaldi 43, 81100, Caserta, Italy.
概括
使用一种新的深度学习方法进行沿海含水层脆弱性评估,准确地绘制出海水入侵风险. 卷积神经网络模型显著超过了以前的方法,在沿海地区确定了高风险区域.
科学领域:
- 水文地质学 水文地质学
- 环境科学 环境科学
- 水资源管理 水资源管理
背景情况:
- 沿海含水层脆弱性评估 (CAVA) 对于管理海水入侵 (SWI) 来说至关重要.
- 像原始GALDIT模型 (OGM) 这样的现有模型需要提高准确性和优化参数.
- 在修改 CAVA 模型权重和速率方面,深度学习的应用尚未得到充分探索.
研究的目的:
- 使用先进的建模技术,调查拉希扬沿海水层对SWI的脆弱性.
- 将混合优化模型 (OGM-BBO,OGM-GWO) 与深度学习方法 (CNN) 的性能进行比较.
- 为有效的沿海资源管理制定准确的脆弱性地图 (VM).
主要方法:
- 应用了原始的GALDIT模型 (OGM) 并使用平均下降精度 (MDA) 评估参数显著性.
- 引入基于生物地理的优化 (BBO) 和灰狼优化 (GWO) 来创建混合OGM-BBO和OGM-GWO模型.
- 开发了一种新的卷积神经网络 (CNN) 算法,以生成基于CNN的漏洞地图 (VM).
主要成果:
- 基于CNN的VM实现了0.982的曲线下的优越面积 (AUC),超过了OGM-BBO (0.794) 和OGM-GWO (0.835).
- 基于CNN的VM确定了41%的含水层对SWI的脆弱性非常高到很高,集中在海岸线附近.
- 32%的含水层表现出非常低至低的脆弱性,主要在南部和西南部地区.
结论:
- 卷积神经网络模型在评估海水入侵的沿海含水层脆弱性方面取得了重大进展.
- 拟议的深度学习方法提供了一个非常准确的工具来识别有风险的地区,帮助土地利用规划者和政策制定者.
- 这种方法可以扩展到其他沿海含水层,提高对含水层脆弱性和污染风险的理解.
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