论文标题
水文时间序列序列使用简单组合预测：大数据测试和对一年前河流可预测性的研究
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
论文作者
论文摘要
提供有用的水文预测对于城市和农业水管理，水力发电，洪水保护和管理，减轻干旱和缓解以及河流盆地规划和管理等至关重要。在这项工作中，我们介绍并评估一种新的简单而灵活的方法，用于水文时间序列预测。该方法依赖于（a）至少两种单独的预测方法和（b）预测的中值组合。评估是通过使用大型数据集由大约600个电台的90年平均河流流动时间序列组成的。这些电台覆盖北美和欧洲的大部分地区，代表了各种气候和集水区，因此可以集体支持基准测试。每个时间序列都应用了五种单独的预测方法和26种引入方法的变体。该应用程序以预测模式为准。各个方法是最后的观察基准，简单的指数平滑，复杂的指数平滑，自动回归的自动回归分数集成的移动平均平均线（ARFIMA）和Facebook的先知，而26个变体是由所有可能组合（通过两个，三个，四个或五个）的所有可能组合定义的。从长远来看，新方法是表现出色的，尤其是当将两个以上的个人预测方法组合在其框架中时。此外，对系统框架中各种水文预测方法的病例形成整合的可能性进行了算法研究和讨论。相关研究涵盖了线性回归分析，旨在在代表性预测性能度量标准与所选河流流量统计值的值之间找到可解释的关系...
Delivering useful hydrological forecasts is critical for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among others. In this work, we present and appraise a new simple and flexible methodology for hydrological time series forecasting. This methodology relies on (a) at least two individual forecasting methods and (b) the median combiner of forecasts. The appraisal is made by using a big dataset consisted of 90-year-long mean annual river flow time series from approximately 600 stations. Covering large parts of North America and Europe, these stations represent various climate and catchment characteristics, and thus can collectively support benchmarking. Five individual forecasting methods and 26 variants of the introduced methodology are applied to each time series. The application is made in one-step ahead forecasting mode. The individual methods are the last-observation benchmark, simple exponential smoothing, complex exponential smoothing, automatic autoregressive fractionally integrated moving average (ARFIMA) and Facebook's Prophet, while the 26 variants are defined by all the possible combinations (per two, three, four or five) of the five afore-mentioned methods. The new methodology is identified as well-performing in the long run, especially when more than two individual forecasting methods are combined within its framework. Moreover, the possibility of case-informed integrations of diverse hydrological forecasting methods within systematic frameworks is algorithmically investigated and discussed. The related investigations encompass linear regression analyses, which aim at finding interpretable relationships between the values of a representative forecasting performance metric and the values of selected river flow statistics...
