CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Trophic state assessment using hybrid classification tree-artificial neural network
Authors
Rhen Anjerome Rañola Bedruz
Ronnie Sabino Concepcion
+4 more
Elmer P. Dadios
Sandy Cruz Lauguico
Pocholo James Mission Loresco
Edwin Sybingco
Publication date
1 March 2020
Publisher
Animo Repository
Doi
Cite
Abstract
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. © 2020, Universitas Ahmad Dahlan. All rights reserved
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Animo Repository - De La Salle University Research
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:animorepository.dlsu.edu.p...
Last time updated on 03/12/2021
International Journal of Advances in Intelligent Informatics
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:ojs.ijain.org:article/408
Last time updated on 09/04/2020
International Journal of Advances in Intelligent Informatics (IJAIN)
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:ojs.ijain.org:article/408
Last time updated on 12/05/2020