A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction
基于外生水文气象变量的河流流量函数自回归预测模型AI 解读 ↓
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基于外生水文气象变量的河流流量函数自回归预测模型
- 研究问题
- 如何利用河流流量曲线的历史滞后与降雨、温度、蒸发等外生气候变量预测未来流量，并刻画预测的不确定性？
- 方法与思路
- 作者构造包含相关滞后及外生变量的函数时间序列模型，进行变量选择，并用非参数 bootstrap 建立逐点预测区间。
- 材料陈述的贡献
- 摘要称，伊拉克三个气象站的数据比较显示预测表现具有竞争力或优于基准，且纳入气候变量改善了预测能力。
- 阅读关注点
- 应核对函数曲线和预测时点的构造、变量选择及验证设计、外生变量在预测时是否可得，以及逐点区间的覆盖率。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未报告误差或覆盖率，三个站点的结果不能直接保证跨地区表现。
原文摘要
In this research, a functional time series model was introduced to predict future realizations of river flow time series. The proposed model was constructed based on a functional time series's correlated lags and the essential exogenous climate variables. Rainfall, temperature, and evaporation variables were hypothesized to have substantial functionality in river flow simulation. Because an actual time series model is unspecified and the input variables' significance for the learning process is unknown in practice, it was employed a variable selection procedure to determine only the significant variables for the model. A nonparametric bootstrap model was also proposed to investigate predictions' uncertainty and construct pointwise prediction intervals for the river flow curve time series. Historical datasets at three meteorological stations (Mosul, Baghdad, and Kut) located in the semi-arid region, Iraq, were used for model development. The prediction performance of the proposed model was validated against existing functional and traditional time series models. The numerical analyses revealed that the proposed model provides competitive or even better performance than the benchmark models. Also, the incorporated exogenous climate variables have substantially improved the modeling predictability performance. Overall, the proposed model indicated a reliable methodology for modeling river flow within the semi-arid region.