高频数据显著提高了点和间隔估计的预测能力
Xin Liu1, Fu-Jun Yue1, Tian-Li Guo2
1Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China.
The Science of the total environment
|December 22, 2023
概括
优化水质监测频率是准确预测溶解氧 (DO) 的关键. 阿里马-加奇模型在低频度的DO评估方面表现出色,每4小时的监测被证明是可靠的水生生态系统健康评估的最佳选择.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 数据科学数据科学数据科学
背景情况:
- 准确的溶氧 (DO) 预测对于水生生态系统的稳定性至关重要.
- 最佳的水质监测频率仍未得到充分研究,以平衡持续评估和成本效益.
研究的目的:
- 开发和比较混合的随机水文和机器学习模型用于DO动态预测.
- 确定最佳的监测频率,以有效地评估水质.
主要方法:
- 开发了一种混合ARIMA-GARCH模型和各种机器学习 (ML) 模型.
- 在不同的监测频率 (如15分钟,每小时,每天) 中评估模型性能.
- 使用R平方和相对可变性指数 (RIW) 度量来评估预测准确度.
主要成果:
- 高频的DO数据显示出比低频数据更大的变化.
- 在低频 DO 预测方面,ARIMA-GARCH 模型的性能优于 ML 模型.
- 增加监测频率显著提高了所有模型的预测准确性.
- 每4小时的监测频率被确定为DO评估的最佳频率.
结论:
- 阿里马-加尔奇模型为低频 DO 预测提供了一个强大的方法.
- 监测频率对DO预测准确性和模型性能产生重大影响.
- 每4小时的监测策略平衡了水质评估的成本和准确性.
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