摘要
赤潮是一种由多因素综合作用引发的生态异常现象,具有突发性及非线性等特点。对其进行预测预报一直是海洋科学研究的热点。探讨了应用人工神经网络原理进行赤潮预测的方法,简要介绍了BP和RBF算法的基本原理,用2种算法对不同海域赤潮生物与环境因子之间非线性和不确定性的复杂关系进行学习训练和预测检验,并与传统的统计方法进行了比较。结果表明:人工神经网络方法在模拟和预测方面优于传统的统计回归模型,具有较强的模拟预测能力及实用性,值得进一步探索。
Red tide is an anomalous ecological phenomenon and is characterized by abruptness and nonlinearity, so the red tide prediction has been a hotspot in the oceanographic studies. In this paper, the application of artificial neural network (ANN) method to the red tide prediction is discussed, and the fundamentals of BP and RBF algorisms are briefly introduced. The two algorisms were used to simulate the relationship between the red tide organsim density and environmental factors in different sea areas, and the comparison between ANN method and traditional statistical method was made. It is shown from the comparison results that the ANN model is better than the statistical model in both simulation and prediction, and has strong simulation and prediction abilities and practicality, so ANN method is worthy of further exploration.
出处
《海洋科学进展》
CAS
CSCD
北大核心
2003年第3期318-324,共7页
Advances in Marine Science
基金
国家"十五"科技攻关项目---赤潮灾害预报技术研究
国家海洋局青年基金项目---神经网络方法在赤潮预警预报中的应用研究(2002214)
