基于不确定性的盐水入侵预测使用集成贝叶斯机器学习建模 (IBMLM) 在深层含水层
Jina Yin1, Yulu Huang1, Chunhui Lu1
1The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China.
Journal of environmental management
|February 23, 2024
概括
本研究引入了一种集成贝叶斯机器学习建模 (IBMLM) 方法,通过考虑模型结构和参数不确定性来改善地下水预测. IBMLM提供比独立模型更准确和可靠的盐水入侵预测.
科学领域:
- 水文地质学 水文地质学
- 机器学习 机器学习
- 环境建模环境建模
背景情况:
- 基于物理的地下水模型是计算密集的.
- 现有的机器学习替代品经常忽略结构不确定性,影响可靠性.
- 盐水入侵 (SWI) 是沿海含水层的一个关键问题.
研究的目的:
- 开发一种灵活的集成贝叶斯机器学习建模 (IBMLM) 方法.
- 从机器学习替代品的结构和参数中明确量化不确定性.
- 提高地下水预测的准确性和可靠性,特别是对于盐水入侵.
主要方法:
- 综合贝叶斯机器学习建模 (IBMLM) 框架.
- 整合了预期最大化 (EM) 算法和贝叶斯模型平均值 (BMA).
- 在IBMLM中应用人工神经网络 (ANN),支持矢量机 (SVM) 和随机森林 (RF).
主要成果:
- IBMLM显示出高预测准确度,R值>0.98和NSE值>0.93.
- 与单个机器学习模型相比,IBMLM提供了更好的预测.
- 该方法有效地预测了没有复杂的物理模拟的盐水入侵.
结论:
- 明确考虑机器学习模型结构的不确定性可以提高预测的准确性和可靠性.
- IBMLM为传统的地下水模型提供了一个计算效率高的替代方案.
- 在数据稀缺的地区,IBMLM框架适用于水文地质建模.
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