A Novel Eutrophication Assessment Models for Aquaculture Water Area via Artificial Neural Networks

作者：Yang, Dazuo; Chen, Fudi; Zhou, Yibing^*

来源：Journal of Computational and Theoretical Nanoscience, 2015, 12(9): 2909-2912.

DOI：10.1166/jctn.2015.4198

• Eutrophication Assessment Models
• Artificial Neural Networks
• General Regression Neural Networks
• Multi-Layer Feed-Forward Neural Networks

In this paper, we use a series of artificial neural networks (ANNs) to develop the eutrophication assessment models for aquaculture water area, and make a series comparisons among previous studies and our research works. Using general regression neural networks (GRNN) and multi-layer feed-forward neural networks (MLFN) can highly improve the eutrophication assessment results. Compared to the back-propagation neural network (BPNN), our GRNN and MLFN models have more robust and precise results. Results show that GRNN and MLFN models can be well-trained for assessing the eutrophication of aquaculture water area.

• 出版日期2015-9
• 单位大连理工大学; 大连海洋大学