Reconstruction of Sparse Stream Flow and Concentration Time‐Series Through Compressed Sensing
Reconstruction of Sparse Stream Flow and Concentration Time‐Series Through Compressed Sensing
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DOI：
10.1029/2022gl101177
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发表时间：
2023-01
影响因子：
5.2
通讯作者：
Kun Zhang;Wasif Bin Mamoon;E. Schwartz;A. Parolari
中科院分区：
文献类型：
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作者：
Kun Zhang;Wasif Bin Mamoon;E. Schwartz;A. Parolari
高频监测水质具有挑战性且成本高昂。鉴于水质信号在频域中通常是“稀疏”的，压缩感知（CS）提供了一种通过有限测量重建高频水质数据的方法。在本研究中，我们研究了河流流量和浓度时间序列的稀疏性，并测试了 CS 的重建。所有流信号均使用 15 分钟离散时间序列变换到傅立叶域进行稀疏处理。水流温度、电导、溶解氧和硝酸盐加亚硝酸盐 (NOx-N) 浓度比排放、浊度和总磷 (TP) 浓度稀疏。 CS 只需 5%–10% 的测量即可有效地重建这些信号。流NOx-N和TP负荷得到了很好的估计，误差分别为-6.6%±3.8%和-9.0%±2.9%，有效采样频率分别为10天和0.4天。为了在环境地球科学和工程领域得到更广泛的应用，CS 可以与降维和优化技术相结合，以实现更有效的采样方案。
Monitoring water quality at high frequency is challenging and costly. Compressed sensing (CS) offers an approach to reconstruct high‐frequency water quality data from limited measurements, given that water quality signals are commonly “sparse” in the frequency domain. In this study, we investigated the sparsity of stream flow and concentration time‐series and tested reconstruction with CS. All stream signals were sparse using 15‐min discrete time‐series transformed to the Fourier domain. Stream temperature, conductance, dissolved oxygen, and nitrate plus nitrite (NOx‐N) concentration were sparser than discharge, turbidity, and total phosphorus (TP) concentration. CS effectively reconstructed these signals with only 5%–10% of measurements needed. Stream NOx‐N and TP loads were well estimated with errors of −6.6% ± 3.8% and −9.0% ± 2.9% with effective sampling frequencies of 10 and 0.4 days, respectively. For broader applications in environmental geosciences and engineering domains, CS can be integrated with dimensionality reduction and optimization techniques for more efficient sampling schemes.