Paper Info
Title
Prediction of Population Dynamics of Bacillariophyta in the Tropical Putrajaya Lake and Wetlands (Malaysia) by a Recurrent Artificial Neural Networks
Abstract
Phytoplankton becomes a concern to the society when it forms a dense growth at water surface known as algae bloom. This paper discusses feasibility of applying recurrent artificial neural network to predict occurrence of selected phytoplankton population the Bacillariophyta population in Putrajaya Lake and Wetlands for one month ahead prediction. The data used are monthly data collected from August 2001 until May 2006. Network performance is measured based on the root mean square error value (RMSE). Input selection is carried out by means of correlation analysis, sensitivity analysis and unsupervised neural network SOM. Better results are achieved for simpler network where variables are selected using method stated above. Thus the capability of neural network model as a predictive tool for tropical lake cannot be disregarded at all.
Year
DOI
Venue
2009
10.1109/ICECS.2009.74
ICECS
Keywords
Field
DocType
recurrent artificial neural network,tropical putrajaya lake,bacillariophyta population,population dynamics,sensitivity analysis,selected phytoplankton population,simpler network,correlation analysis,unsupervised neural network,network performance,recurrent artificial neural networks,neural network model,monthly data,data collection,recurrent neural network,microorganisms,root mean square error,water pollution,unsupervised learning,algae bloom,artificial neural networks,population dynamic,correlation,recurrent neural networks
Phytoplankton,Population,Computer science,Recurrent neural network,Mean squared error,Wetland,Unsupervised learning,Artificial intelligence,Artificial neural network,Machine learning,Network performance
Conference
ISBN
Citations 
PageRank 
978-1-4244-5591-1
1
0.37
References 
Authors
1
3
Name
Order
Citations
PageRank
Sorayya Malek1252.44
Aishah Salleh2221.70
Mohd Sapiyan Baba3517.61