Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Shanwei Liu,Jinghui Jiang,Yue Jiao,Qinting Sun,Shaukat Ali

doi:10.1155/2021/6618135

Shanwei Liu, Jinghui Jiang + Show 3 more

Open Access

https://doi.org/10.1155/2021/6618135

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Abstract

The South China Sea is China’s largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea.

Highlights

Material and MethodsE seasonal autoregressive integrated moving average (SARIMA) model evolved based on the ARIMA model, which takes into account the seasonal factors of the time series [20,21,22]
Introduction e South China Sea isChina’s largest marginal sea and is a transportation hub for maritime energy transportation; it has abundant reserves of oil and gas resources [1]
E research area of this paper is the South China Sea and it is shown in Figure 3. e coordinates are 110°–119°E, 14°–23°N, and the total area is approximately 1.19 million square kilometers. e area is located between the Pacific Ocean and the Indian Ocean, and it includes many important shipping lanes for material transportation

Summary

Material and Methods

E SARIMA model evolved based on the ARIMA model, which takes into account the seasonal factors of the time series [20,21,22]. It adopts the method of seasonal difference to estimate parameters and can effectively predict time series with seasonality, trend, and periodicity. Where yt is the time series, μt is the random term, Φp(B) is the nonseason AR(p) part, Φp(BS) is the season AR(P) part, (1 − BS)D is the d-order progressive difference, θq(B) is the nonseason MA(q) part, and ΘQ(BS) is the season MA (Q) part

South china sea islands Land

SARIMA model

LSTM model

Observed data Predicted results