Nearest neighbor time series bootstrap for generating influent water quality scenarios
Nearest neighbor time series bootstrap for generating influent water quality scenarios
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DOI：
10.1007/s00477-019-01762-3
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发表时间：
2020-01
影响因子：
4.2
通讯作者：
W. Raseman;B. Rajagopalan;J. Kasprzyk;W. Kleiber
中科院分区：
文献类型：
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作者：
W. Raseman;B. Rajagopalan;J. Kasprzyk;W. Kleiber
了解进水水质的变异性对于饮用水系统的长期规划至关重要。为了量化可变性和生成真实的影响场景，我们提出了一种基于k-最近邻(k-NN)自举重采样的非参数时间序列方法。K-NN方法在给定时间重采样以“特征向量”为条件的历史数据，以在随后的时间生成值。我们修改了这个算法，将随机扰动添加到重采样值中，以生成历史记录中未观察到的现实极值。K-NN被广泛应用于随机水文学和水文气候学中；然而，它在这里适用于水处理的多变量、数据有限的背景。为了检验算法的性能，我们将其应用于来自科罗拉多州波德河的11年的每月水质数据集，包括碱度、温度、总有机碳和pH值。我们发现，k-NN模拟捕捉到了历史记录的相关分布统计信息，这表明该算法产生了真实和多样的场景。当与建模和优化结合使用时，这些情景有可能提高饮用水系统的可持续性、恢复力和效率。
Understanding influent water quality variability is essential for the long-term planning of potable water systems. To quantify variability and generate realistic influent scenarios, we propose a nonparametric time series approach based on k-nearest neighbor (k-NN) bootstrap resampling. The k-NN approach resamples historical data conditioned on a “feature vector” at a given time to generate values at subsequent times. We modified this algorithm by adding random perturbations to the resampled values to generate realistic extremes unobserved in the historical record. k-NN is widely used in stochastic hydrology and hydroclimatology; however, it is adapted here for the multivariate, data-limited context of water treatment. To examine the performance of the algorithm, we applied it to an eleven-year, monthly water quality dataset of alkalinity, temperature, total organic carbon, and pH from the Cache la Poudre River in Colorado. We found that the k-NN simulations captured the relevant distributional statistics of the historical record, which suggests that the algorithm produces realistic and varied scenarios. When used in conjunction with modeling and optimization, these scenarios have the potential to improve the sustainability, resilience, and efficiency of potable water systems.