Li Xiao, Zhou Shijian, Wang Fengwei, Fu Laiying
School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, 330013, China.
Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang, 330013, China.
Sci Rep. 2024 Feb 24;14(1):4560. doi: 10.1038/s41598-024-55266-4.
Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine-cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R) are adopted. For six stations, the RMSE, MAE, MAPE and R of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change.
准确预测海平面高度对于政府评估沿海地区的海平面风险至关重要。然而，由于海平面变化数据具有非线性、时变和高度不确定性的特点，海平面预测具有挑战性。为了提高海平面预测的准确性，本文使用了一种名为麻雀搜索算法（SSA）的新型群智能算法，该算法可以模仿麻雀的觅食行为和反捕食行为来确定最优解。为了避免算法陷入局部最优情况，本文将正弦余弦算法和柯西变异策略融入到SSA中，得到了一种名为SCSSA的算法。SCSSA用于优化CNN-BiLSTM（卷积神经网络与双向长短期记忆神经网络相结合）模型的参数值；最后，提出了一种组合神经网络模型（名为SCSSA-CNN-BiLSTM）。本文使用中国沿海七个潮汐站的时间序列数据进行实验分析。首先，将SCSSA-CNN-BiLSTM模型与CNN-BiLSTM模型进行比较，以预测汕尾站的时间序列数据。对于训练集和测试集数据，SCSSA-CNN-BiLSTM模型在所有评估指标上均优于其他模型。此外，使用包括SCSSA-CNN-BiLSTM模型在内的其余六个潮汐站数据集和五个神经网络模型，进一步研究所提出的预测模型的性能。采用了四个评估指标，包括均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和决定系数（R）。对于六个站点，SCSSA-CNN-BiLSTM模型的RMSE、MAE、MAPE和R分别在20.9217 ~ 27.8427毫米、9.4770 ~ 17.8603毫米、0.1322% ~ 0.2482%和0.9119 ~ 0.9759之间。实验分析结果表明，SCSSA-CNN-BiLSTM模型在所有站点都能做出有效的预测，且预测性能优于其他模型。尽管SCSSA算法的组合可能会增加模型的复杂度，但所提出的模型确实是一种用于预测海平面变化的具有良好准确性和鲁棒性的新预测方法。
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