Trophic state assessment using hybrid classification tree-artificial neural network

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Trophic state assessment using hybrid classification tree-artificial neural network
 
Creator Concepcion II, Ronnie Sabino
Loresco, Pocholo James Mission
Bedruz, Rhen Anjerome Rañola
Dadios, Elmer Pamisa
Lauguico, Sandy Cruz
Sybingco, Edwin
 
Subject Aquaponics; Assessment; Artificial neural network; Modelling tree; Trophic state
 
Description The trophic state is one of the significant environmental impacts that must be monitored and controlled in any aquatic environment. This phenomenon due to nutrient imbalance in water strengthened with global warming, inhibits the natural system to progress. With eutrophication, the mass of algae in the water surface increases and results to lower dissolved oxygen in the water that is essential for fishes. Numerous limnological and physical features affect the trophic state and thus require extensive analysis to asses it. This paper proposed a model of hybrid classification tree-artificial neural network (CT-ANN) to assess the trophic state based on the selected significant features. The classification tree was used as a multidimensional reduction technique for feature selection, which eliminates eight original features. The remaining predictors having high impacts are chlorophyll-a, phosphorus and Secchi depth. The two-layer ANN with 20 artificial neurons was constructed to assess the trophic state of input features. The neural network was modeled based on the key parameters of learning time, cross-entropy, and regression coefficient. The ANN model used to assess trophic state based on 11 predictors resulted in 81.3% accuracy. The modeled hybrid classification tree-ANN based on 3 predictors resulted to 88.8% accuracy with a cross-entropy performance of 0.096495. Based on the obtained result, the modeled hybrid classification tree-ANN provides higher accuracy in assessing the trophic state of the aquaponic system.
 
Publisher Universitas Ahmad Dahlan
 
Contributor Department of Science and Technology
De La Salle University
 
Date 2020-03-29
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/408
10.26555/ijain.v6i1.408
 
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 1 (2020): March 2020; 46-59
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/408/ijain_v6i1_p46-59
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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