Trophic state assessment using hybrid classification tree-artificial neural network - Ronnie Sabino Concepcion II; Pocholo James Mission Loresco; Rhen Anjerome Rañola Bedruz; Elmer Pamisa Dadios; Sandy Cruz Lauguico; Edwin Sybingco (Universitas Ahmad Dahlan, 2020)
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.