Publication: A comparison between neural network based and fuzzy logic models for chlorophll-a estimation
Date
2010
Authors
Malek S.
Salleh A.
Ahmad S.M.S.
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Abstract
This paper describes the application of two novel computational methods such as fuzzy logic and supervised artificial neural network (ANN) to model algal biomass in tropical Putrajaya Lake and Wetlands (Malaysia). Limnological time series data collected from 2001 until 2004 was utilized using input parameters such as water temperature, pH, secchi depth, dissolved oxygen, ammoniacal nitrogen and nitrate nitrogen. Performance measure for the models developed was in terms of root mean square error (RMSE). Both models developed gave similar result with models developed using fuzzy logic approach performed slightly better compared to feed-forward artificial neural network model. � 2010 IEEE.
Description
Keywords
Aritificial neural network , Chlorophyll-a , Fuzzy logic , Chlorophyll , Computer applications , Dissolution , Dissolved oxygen , Fuzzy logic , Fuzzy systems , Porphyrins , Time series , Algal biomass , Ammoniacal nitrogen , Artificial Neural Network , Chlorophyll a , Feed-forward artificial neural networks , Fuzzy logic approach , Fuzzy logic model , Input parameter , Malaysia , Nitrate nitrogen , Performance measure , Root mean square errors , Secchi depth , Time-series data , Water temperatures , Neural networks