We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Improved Prediction of Local Significant Wave Height by Considering the Memory of Past Winds.
- Authors
Zhang, Shaotong; Yang, Zhen; Zhang, Yaqi; Zhao, Shangrui; Wu, Jinran; Wang, Chenghao; Wang, You‐Gan; Jeng, Dong‐Sheng; Nielsen, Peter; Li, Guangxue; Li, Sanzhong
- Abstract
Wave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high‐precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data‐driven approach. The proposed model realized high accuracy as it solves the problem that the Hs falls too fast during the wind‐decreasing periods. It was tackled by considering the remaining influence of historical vw on the present Hs via incorporating a memory curve of the past wind effect. This innovative approach significantly improves the prediction (R2 from 0.60 to 0.83). The winds in the past 24 hr still left an influence on the waves at the observation site although the influence decreases with time. Physically, it is an implicit but simpler consideration of wind fetch/duration. Further data modeling experiments indicated that the decisive factor for the Hs at the site is the wind speed. Wind directions slightly improve the prediction, indicating that waves are slightly affected by the underwater seabed slope along different wind directions, and northwest winds cause the strongest waves at the site. Adding atmospheric pressure or water depth even reduces the accuracy, which indicated that storm surges and wave deformations under different tide levels have a weak impact on Hs. The proposed local wave model can be easily constructed with available wind and wave data, making it expandable to other regions dominated by wind waves. Plain Language Summary: Wave conditions in the ocean are essential for engineering applications. Due to the influence of geographical environment, topography, and geomorphology, the wave conditions of a certain station in the ocean often have certain rules to follow. This paper attempts to establish a high‐precision prediction model for local significant wave height of wind waves through long‐term in situ observations and machine learning methods. The introduction of a "memory curve" into the conventional Support Vector Regression model significantly improved the prediction accuracy. The physical meaning of the "memory curve" corresponds to the residual impact of historical wind speed, which is the implicit expression of the effects of wind duration/fetch in wave development. The proposed method can be easily applied to other sea areas dominated by wind waves. Key Points: A high‐accuracy local wind wave height model is built based on wind speedA memory curve is incorporated into Support Vector Regression to improve the prediction significantlyInfluencing factors of wave height are explored through data modeling experiments
- Subjects
STORM surges; WIND waves; WATER waves; OCEAN waves; WIND speed; WATER depth; MEMORY
- Publication
Water Resources Research, 2023, Vol 59, Issue 8, p1
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/2023WR034974