基于R-OSELM的海洋环境数据在线预测

为及时辨识海洋环境的变化趋势和降低长期累积的海洋环境数据对预测模型的影响,提出一种基于循环在线顺序极限学习机(Recurrent Online Sequential Extreme Learning Machine R-OSELM)的海洋环境数据在线预测模型.采用完全在线的方法初始化海洋环境数据训练集,通过在线顺序极限学习机算法对已有的海洋环境数据进行逐块输入,利用极限学习机的自动编码技术与一种归一化方法对输入权重循环处理,实现预测模型的在线更新,最后完成对海洋环境数据的在线预测.使用该模型对溶解氧、叶绿素a、浊度、蓝绿藻进行预测,结果表明R-OSELM模型的预测精度高于对比模型,确定其具备海洋环境数据在线预测能力,可为海洋水域水体富营养化与海洋环境污染预警提供参考.

Online prediction of marine environment data based on R-OSELM

In order to timely identify the changing trend of marine environment and reduce the influence of long-term accumulated marine environment data on prediction model,an online prediction model of marine environment data based on recurrent online sequential extreme learning machine (R-OSELM) is proposed.The marine environment data training set is initialized by an online method,the existing marine environment data is input block by block via online sequential extreme learning machine algorithm,and the input weight is cyclically processed by automatic coding technology of extreme learning machine and a normalized method,which realize the online update of the prediction model.Finally,online prediction of marine environment data is completed.The model is then used to predict dissolved oxygen,chlorophyll A,turbidity,and blue-green algae.The results show that the prediction accuracy of R-OSELM model is better than that of the comparison model.It is verified that the proposed R-OSELM model is capable of online prediction of marine environment data,which can provide support for early warning of marine eutrophication and other marine environmental pollution.