基于深度学习的替代品,用于地下水抽取计划的多目标优化,以管理海水入沿海含水层的入侵
Dilip Kumar Roy1, Bithin Datta2
1Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Gazipur-1701, Bangladesh.
Journal of environmental management
|February 22, 2025
概括
深度学习替代模型优化了地下水的抽水,以应对沿海含水层的盐分入侵. 多重目标可行性增强颗粒群集优化 (MOFEPSO) 方法在可持续水资源管理方面被证明是最有效的.
科学领域:
- 水地质学和水资源管理
- 环境科学中的人工智能
- 计算流体动力学和模拟计算
背景情况:
- 沿海含水层面临着度入侵的重大挑战,威胁着地下水的可持续性.
- 有效地优化地下水抽水是管理这些资源的必要条件.
- 替代模型 (SMs) 为管理任务提供了复杂的地下水模拟的计算效率高的替代方案.
研究的目的:
- 开发和比较六种基于深度学习 (DL) 的替代模型,以优化地下水抽水.
- 用先进的算法解决结合模拟-优化问题.
- 为了验证和选择实际水资源管理中表现最好的方法.
主要方法:
- 开发和比较六个基于DL的SM:前神经网络,长期短期记忆 (LSTM),双向LSTM (Bi-LSTM),预测层LSTM (亲LSTM) 和门式反复单元 (GRU).
- 应用多目标基因算法与控制的精英主义 (CEMOGA) 和多目标可行性增强的粒子群集优化 (MOFEPSO) 来获得帕雷托最佳解决方案.
- 使用数值模型验证最佳排时间表,并通过简单的增量权重 (SAW) 和以理想解决方案相似度为优先顺序的技术 (TOPSIS) 选择最佳妥协解决方案.
主要成果:
- 最好的基于DL的SM在不同监测位置提供了高准确度和低误差指标的准确预测.
- 在导出最佳排时间表方面,MOFEPSO表现优于CEMOGA,相对误差百分比最小 (0-0.025%).
- 选择的最佳妥协解决方案为水资源管理者面临相互竞争的目标提供了实际指导.
结论:
- 深度学习替代模型,特别是与MOFEPSO相结合时,为优化沿海水层地下水抽取提供了精确有效的方法.
- 经过验证的方法为可持续的水资源规划提供了宝贵的见解,并且可以适应各种地下水管理挑战.
- 这项研究提高了管理度入侵的能力,并确保了地下水的长期可持续性.
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