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A Novel Method for Regional Short-Term Forecasting of Water Level

by 1, 2,*, 1, 1, 3,4 and 1,5,6,*
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan 250013, China
Zhejiang Institute of Hydraulics & Estuary (Zhejiang Institute of Marine Planning and Design), Hangzhou 310020, China
Key Laboratory of Estuary and Coast of Zhejiang Province, Hangzhou 310020, China
Key Laboratory of Oceanic Surveying and Mapping, Ministry of Natural Resources of China, Qingdao 266590, China
Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
Authors to whom correspondence should be addressed.
Academic Editor: Gary B. Griggs
Water 2021, 13(6), 820;
Received: 11 December 2020 / Revised: 11 March 2021 / Accepted: 14 March 2021 / Published: 17 March 2021
The water level forecasting system represented by the hydrodynamic model relies too much on the input data and the forecast value of the boundary, therefore introducing uncertainty in the prediction results. Tide tables ignore the effect of the residual water level, which is usually significant. Therefore, to solve this problem, a water level forecasting method for the regional short-term (3 h) is proposed in this study. First, a simplified MIKE21 flow model (FM) was established to construct the regional major astronomical tides after subdividing the model residuals into stationary constituents (surplus astronomical tides, simulation deviation) and nonstationary constituents (residual water level). Harmonic analysis (HA) and long short-term memory (LSTM) were adopted to forecast these model residuals, respectively. Finally, according to different spatial background information, the prediction for each composition was corrected by the inverse distance weighting (IDW) algorithm and its improved IDW interpolation algorithm based on signal energy and the spatial distance (IDWSE) from adjacent observation stations to nonmeasured locations. The developed method was applied to Narragansett Bay in Rhode Island. Compared with the assimilation model, the root-mean-square error (RMSE) of the proposed method decreased from 12.3 to 5.0 cm, and R2 increased from 0.932 to 0.988. The possibility of adding meteorological features into the LSTM network was further explored as an extension of the prediction of the residual water level. The results show that the accuracy was limited to a moderate level, which is related to the difficulty presented by using only wind features to completely characterize the regional dynamic energy equilibrium process. View Full-Text
Keywords: water level forecast; LSTM network; Narragansett Bay; hydrodynamic model water level forecast; LSTM network; Narragansett Bay; hydrodynamic model
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MDPI and ACS Style

Tu, Z.; Gao, X.; Xu, J.; Sun, W.; Sun, Y.; Su, D. A Novel Method for Regional Short-Term Forecasting of Water Level. Water 2021, 13, 820.

AMA Style

Tu Z, Gao X, Xu J, Sun W, Sun Y, Su D. A Novel Method for Regional Short-Term Forecasting of Water Level. Water. 2021; 13(6):820.

Chicago/Turabian Style

Tu, Zejie, Xingguo Gao, Jun Xu, Weikang Sun, Yuewen Sun, and Dianpeng Su. 2021. "A Novel Method for Regional Short-Term Forecasting of Water Level" Water 13, no. 6: 820.

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