An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Xiao Li; Shijian Zhou; Fengwei Wang; Laiying Fu

doi:10.1038/s41598-024-55266-4

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Sci Rep. 2024 Feb 24;14(1):4560. doi: 10.1038/s41598-024-55266-4.

Authors

Xiao Li^{1

2}, Shijian Zhou³, Fengwei Wang⁴, Laiying Fu⁵

Affiliations

¹ School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, 330013, China.
² Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang, 330013, China.
³ Nanchang Hangkong University, Nanchang, 330063, China. shjzhou@nchu.edu.cn.
⁴ State Key Laboratory of Marine Geology, Tongji University, Shanghai, 200092, China.
⁵ School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, 330013, China.

Abstract

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 (R²) are adopted. For six stations, the RMSE, MAE, MAPE and R² 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.

Abstract

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