Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks-Reference-Cited by-同舟云学术

Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Published:2007-06 Issue:2 Volume:2 Page:184-192
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Li Hongbin,Hou Guoxiang,Dakui Feng,Xiao Bangding,Song Lirong,Liu Yongding

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modeling,Modeling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference26 articles.

1. Asymptotic statistical theory of overtraining and cross-validation;Amari;IEEE Transactions on Neural Networks,1997

2. Optimal use of regularization and cross validation in neural network modeling;Chen,1999

3. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city;Dimopoulos;Ecological Modelling,1999

4. Regularization theory and neural networks architectures;Girosi;Neural Computation,1995

5. Back-propagation neural networks for modeling complex systems;Goh;Artificial Intelligence in Engineering,1995

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Simulation and control of the cyanobacterial bloom biomass in a typical plateau lake based on the logistic growth model: A case study of Xingyun Lake;Ecological Informatics;2024-09

2. Smart solutions for dissolved oxygen control in semi-batch fermenters: A machine learning approach;Desalination and Water Treatment;2024-01

3. Prediction of algal bloom using a combination of sparse modeling and a machine learning algorithm: Automatic relevance determination and support vector machine;Ecological Informatics;2023-12

4. Quantification of river total phosphorus using integrative artificial intelligence models;Ecological Indicators;2023-09

5. The Application of an Artificial Neural Network to Quantify Anthropogenic and Climatic Drivers in Coastal Phytoplankton Shift;Frontiers in Marine Science;2022-07-18