Improved Particle Swarm Optimization for Sea Surface Temperature Prediction

Qi He, Cheng Zha, Wei Song, Zengzhou Hao, Yanling Du, Antonio Liotta and Cristian Perra
Additional contact information
Qi He: Department of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Cheng Zha: Department of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Wei Song: Department of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Zengzhou Hao: State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Yanling Du: Department of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Antonio Liotta: School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Cristian Perra: Department of Electrical and Electronic Engineering, University of Cagliari, Via Marengo, 2, 09100 Cagliari, Italy

Energies, 2020, vol. 13, issue 6, 1-18

Abstract: The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM).

Keywords: sea surface temperature; sea surface temperature prediction; similarity measure; support vector machine; particle swarm optimization; local search (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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