Kılıç, HürevrenKilic, HurevrenSoyupak, SelcukGurbuz, HasanKivrak, ErsinComputer Engineering2024-07-052024-07-05200661574-95411878-051210.1016/j.ecoinf.2006.09.0022-s2.0-37849186103https://doi.org/10.1016/j.ecoinf.2006.09.002https://hdl.handle.net/20.500.14411/1053Kilic, Hurevren/0000-0002-9058-0365; KILIC, HUREVREN/0000-0003-2647-8451Artificial 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 preprocessing 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. (c) 2006 Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/openAccessprimary productiondominating speciesautomata networksartificial neural networksprincipal component analysisArticleQ1Q114431439WOS:000244894400009