Prediction model of salt marsh wetland salinity based on improved variational mode decomposition and least square support vector machine

Qizheng Zhao; Guohui Li

doi:10.1117/12.3049750

8 November 2024 Prediction model of salt marsh wetland salinity based on improved variational mode decomposition and least square support vector machine

Qizheng Zhao, Guohui Li

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162F (2024) https://doi.org/10.1117/12.3049750
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Salinity prediction in salt marsh wetland contributes to ecological conservation and management, guides agricultural and fishery activity, and promotes sustainable development of wetland ecosystem. Aiming at the characteristics of wetland salinity, which is nonlinear and susceptible to seasonal influence, prediction model of salt marsh wetland salinity based on improved variational mode decomposition and least square support vector machine is proposed. Aiming at the problem that the penalty factor and the number of decomposition layers of variational mode decomposition (VMD) must be set manually, VMD based on red-tailed hawk algorithm (RTH), named RTH-VMD, is proposed. Aiming at the problem of selecting the kernel function parameters and penalty factor of least square support vector machine (LSSVM), LSSVM based on red-tailed hawk algorithm (RTH), named RTH-LSSVM, is proposed. Firstly, decompose salt marsh wetland salinity by RTH-VMD to obtain several intrinsic mode functions (IMFs). Then, predict each IMFs by RTH-LSSVM and each IMFs prediction error by autoregressive integrated moving average (ARIMA). Finally, reconstruct the prediction result and error prediction result to obtain the final prediction result. Considering geographical location and data accuracy, the salinity data of natural and restored wetland stream in Cape Cod, Massachusetts in 2019 are selected for forecasting experiment. Experimental result shows that the performance of the proposed prediction model is significantly better than that of other prediction models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Qizheng Zhao and Guohui Li "Prediction model of salt marsh wetland salinity based on improved variational mode decomposition and least square support vector machine", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162F (8 November 2024); https://doi.org/10.1117/12.3049750

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KEYWORDS

Modal decomposition

Support vector machines

Performance modeling

Data modeling

Autoregressive models

Information operations

Mathematical optimization

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