Automata networks as preprocessing technique of artificial neural network in estimating primary production and dominating phytoplankton levels in a reservoir: An experimental work
| Autor: | Hasan Gürbüz, Selçuk Soyupak, Hurevren Kilic, Ersin Kivrak |
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| Rok vydání: | 2006 |
| Předmět: |
Ecology
Artificial neural network business.industry Applied Mathematics Ecological Modeling Machine learning computer.software_genre Computer Science Applications Automaton Computational Theory and Mathematics Modeling and Simulation Phytoplankton Principal component analysis Redundancy (engineering) Preprocessor Model development Experimental work Artificial intelligence Biological system business computer Ecology Evolution Behavior and Systematics Mathematics |
| Zdroj: | Ecological Informatics. 1:431-439 |
| ISSN: | 1574-9541 |
| DOI: | 10.1016/j.ecoinf.2006.09.002 |
| Popis: | Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subsequent ANN model development for Demirdoven Dam Reservoir. The primary purpose of using pre-processing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency. The data were collected monthly over two years. The applications have yielded following results: The correlation coefficients (r values) between predicted and observed counts were as high as 0.83, 0.87, 0.83 and 0.88 for Cyclotella ocellata, Sphaerocystis schroeteri, Staurastrum longiradiatum counts, and Chlorophyll-a (Chl-a) concentrations respectively with AN. The performance of AN based pre-processing technique was compared with the performance of a well-known pre-processing technique, namely Principle Component Analysis(PCA), experimentally. r values between the predicted and observed C. ocellata, S. schroeteri and S. longiradiatum counts, and (Chl-a) were as high as 0.80, 0.86, 0.81 and 0.86 respectively with PCA. |
| Databáze: | OpenAIRE |
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