Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning

Article


Raj, Nawin. 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning." Mathematics. 10 (23). https://doi.org/10.3390/math10234533
Article Title

Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning

ERA Journal ID213646
Article CategoryArticle
AuthorsRaj, Nawin
Journal TitleMathematics
Journal Citation10 (23)
Article Number4533
Number of Pages23
Year2022
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2227-7390
Digital Object Identifier (DOI)https://doi.org/10.3390/math10234533
Web Address (URL)https://www.mdpi.com/2227-7390/10/23/4533
Abstract

Sea level rise (SLR) in small island countries such as Kiribati and Tuvalu have been a significant issue for decades. There is an urgent need for more accurate and reliable scientific information regarding SLR and its trend and for more informed decision making. This study uses the tide gauge (TG) dataset obtained from locations in Betio, Kiribati and Funafuti, Tuvalu with sea level corrections for vertical land movement (VLM) at these locations from the data obtained by the Global Navigation Satellite System (GNSS) before the sea level trend and rise predictions. The oceanic feature inputs of water temperature, barometric pressure, wind speed, wind gust, wind direction, air temperature, and three significant lags of sea level are considered in this study for data modeling. A new data decomposition method, namely, successive variational mode decomposition (SVMD), is employed to extract intrinsic modes of each feature that are processed for selection by the Boruta random optimizer (BRO). The study develops a deep learning model, namely, stacked bidirectional long short-term memory (BiLSTM), to make sea level (target variable) predictions that are benchmarked by three other AI models adaptive boosting regressor (AdaBoost), support vector regression (SVR), and multilinear regression (MLR). With a comprehensive evaluation of performance metrics, stacked BiLSTM attains superior results of 0.994207, 0.994079, 0.988219, and 0.899868 for correlation coefficient, Wilmott’s Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for Kiribati, and with values of 0.996806, 0.996272, 0.992316, and 0.919732 for correlation coefficient, Wilmott’s Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for the case of Tuvalu. It also shows the lowest error metrics in prediction for both study locations. Finally, trend analysis and linear projection are provided with the GNSS-VLM-corrected sea level average for the period 2001 to 2040. The analysis shows an average sea level rate rise of 2.1 mm/yr for Kiribati and 3.9 mm/yr for Tuvalu. It is estimated that Kiribati and Tuvalu will have a rise of 80 mm and 150 mm, respectively, by the year 2040 if estimated from year 2001 with the current trend.

KeywordsBoruta random forest optimizer (BRFO); sea level rise (SLR); Global Navigation Satellite System (GNSS); vertical land movement (VLM); bidirectional long short-term memory (BiLSTM); successive variational mode decomposition (SVMD)
Article Publishing Charge (APC) FundingResearcher
ANZSRC Field of Research 2020410199. Climate change impacts and adaptation not elsewhere classified
490199. Applied mathematics not elsewhere classified
Byline AffiliationsSchool of Mathematics, Physics and Computing
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