基于WSN的数据挖掘技术在海洋预测中的应用

Application of Data Mining Technology Based on Wireless Sensor Networks in Oceanographic Forecasting

论文旨在提出一种理想的海洋趋势预测方法。为此,提出了一种基于无线传感器网络和计算机技术的海洋水文精确监测方案。然后,利用支持向量回归算法对传感器网络采集的数据进行处理。为了获得最优的算法参数,引入粒子群算法,通过粒子间的相互竞争来寻找全局最优解。在此基础上,根据纽约港的水文情况,建立了海洋水文数据采集与观测系统。然后,利用传统的支持向量回归和提出的方法,基于水温、盐度等指标预测海洋的变化趋势。结果表明,该算法提高了无线传感器网络的数据利用率,取得了良好的预测效果。这项研究为先进技术在海洋预报中的应用提供了重要的见解。

This paper aims to present a desirable prediction method for oceanographic trends.Therefore,an online monitoring scheme is prepared to collect the accurate oceanographic hydrological data based on wireless sensor network(WSN)and computer technology.Then,the data collected by the WSN are processed by support vector regression algorithm.To obtain the most im portant parameters of the algorithm,the particle swarm optimization is introduced to search for the global optimal solution through the coope⁃tition between the particles.After that,an oceanographic hydrological data collection and observation system is created based on the hydrological situation of New York harbour.Then,the traditional support vector regression and the proposed method are applied to predict the oceanographic trends based on water temperature,salinity and other indices.The results show that the proposed algo⁃rithm enhances the data utilization rate of the WSN,and achieves good prediction accuracy.The research provides important in⁃sights into the application of advanced technology in oceanographic forecast.