Neural network and genetic programming for modelling coastal algal blooms
Neural network and genetic programming for modelling coastal algal blooms
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DOI：
10.1504/ijep.2006.011208
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发表时间：
2006-01-01
影响因子：
0.7
通讯作者：
Chau, Kwok-Wing
中科院分区：
文献类型：
--
作者：
Muttil, Nitin;Chau, Kwok-Wing
近年来，人工神经网络 (ANN) 等机器学习 (ML) 技术越来越多地用于模拟藻华动态。在本文中，我们选择遗传编程（GP）与人工神经网络一起对香港吐露港的藻华进行建模和预测。对经过训练的 ANN 权重以及 GP 演化方程的研究表明，它们正确地识别了具有生态意义的变量。对各种 ANN 和 GP 情景的分析表明，仅使用叶绿素-a 作为输入即可获得对藻类生物量长期趋势的良好预测。结果表明，使用双周数据可以很好地模拟藻类生物量的长期趋势，但不太适合给出短期藻华预测。
In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The study of the weights of the trained ANN and also the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of various ANN and GP scenarios indicates that good predictions of long-term trends in algal biomass can be obtained using only chlorophyll-a as input. The results indicate that the use of biweekly data can simulate long-term trends of algal biomass reasonably well, but it is not ideally suited to give short-term algal bloom predictions.