Abstract Forecasting water level is an extremely important task as it allows to mitigate the effects of floods, reduce and prevent disasters. Physically based models often give good results but they require expensive computational time and various types of hydro-geomorphological data to develop the forecasting system. Alternatively, data driven forecasting models are usually faster and easier to build. During the past decades, statistical machine learning (ML) methods have greatly contributed to the advancement of data driven forecasting systems that provide cost-effective solutions and better performance. Meanwhile, Autoregressive integrated moving average (ARIMA) is one of the famous linear statistical models for time series forecasting. In this paper, we propose a hybrid approach that takes advantages of linear and nonlinear models. The proposed method combines statistical machine learning algorithms and ARIMA for forecasting water level. Observed water level of the Red river at the Vu Quang, Hanoi (3 hourly sampled from 2008 to 2017) and Hung Yen hydrological stations (hourly collected data from 2008 to 4/2015) are used to evaluate the performance of different methods. Experimental results on these 3 real big datasets show the effectiveness of our proposed hybrid models.

中文翻译：

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将统计机器学习模型与 ARIMA 结合用于水位预测：以红河为例

摘要 预测水位是一项极其重要的任务，因为它可以减轻洪水的影响，减少和预防灾害。基于物理的模型通常会给出良好的结果，但它们需要昂贵的计算时间和各种类型的水文地貌数据来开发预测系统。或者，数据驱动的预测模型通常更快、更容易构建。在过去的几十年中，统计机器学习 (ML) 方法极大地促进了数据驱动预测系统的发展，这些系统提供了具有成本效益的解决方案和更好的性能。同时，自回归积分移动平均（ARIMA）是著名的时间序列预测线性统计模型之一。在本文中，我们提出了一种利用线性和非线性模型优势的混合方法。所提出的方法结合了统计机器学习算法和 ARIMA 来预测水位。河内武广（2008 年至 2017 年每小时采样 3 小时）和兴安水文站（2008 年至 4/2015 年每小时收集的数据）的红河观测水位用于评估不同方法的性能。在这 3 个真实的大数据集上的实验结果表明了我们提出的混合模型的有效性。