Improved Particle Swarm Optimization for Sea Surface Temperature Prediction

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).

A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems

“…Other use cases of similarity measures in the literature include launch vehicle flight data [25], sea surface temperature data [26], text-based network data for intrusion detection [27], network characteristics to detect spoofing attacks [28], acceleration data for chatter detection [29], clinical case data in medicine [30], [31], and financial market stream data [32]. An evaluation of several use cases, such as face and handwriting recognition or use cases in biology, astronomy, medicine, and robotics, is performed by Górecki et al [33], Ding et al [34], Wang et al [35], Serrà et al [36], and Bagnall et al [37].…”

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

“…Standard PSO has a maximum velocity limit, and the search range is limited to the vicinity of the current position, which makes it easy to fall into local minima [45,46]. In contrast, quantum particle swarm optimization (QPSO) has a better overall search capability by finding the particle position through the Monte Carlo method using the wave function to represent the particle state, and the particles can be searched throughout the space [47,48].…”

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

“…As the trend of global warming steadily increases, it is crucial to plan energy consumption rationally and predict climate security risks. Most researchers opt to directly predict future temperature trends as a reference for policymakers, starting with machine learning predictions, such as using DTW, SVM, and LSPSO to predict sea surface temperatures [9], and later evolving towards deep learning, such as constructing Memory Graph Convolutional Neural Networks for predicting sea surface temperatures [10] and establishing different types of LSTM to predict sea surface temperatures [11].…”