Water Quality Index Estimation Model for Aquaculture System Using Artificial Neural Network
محورهای موضوعی : Pattern Analysis and Intelligent Systems
Taliha Folorunso
1
(Department of Mechatronics Engineering, School of Electrical Engineering and Technology, Federal University of Technology Minna Nigeria)
Musa Aibinu
2
(Department of Mechatronics Engineering, Federal University of Technology, Minna, Nigeria)
Jonathan Kolo
3
(Department of Electrical Engineering, Federal University of Technology, Minna)
Suleiman Sadiku
4
(Department of Water Resources, Aquaculture, and fisheries Technology, Federal University of Technology, Minna)
Abdullahi Orire
5
(Department of Water Resources, Aquaculture and Fisheries Technology, Federal University of Technology, Minna)
کلید واژه: Water Quality Index (WQI), Artificial Neural Network (ANN), Dissolved Oxygen (DO), Aquaculture, WQI Estimation,
چکیده مقاله :
Water Quality plays an important role in attaining a sustainable aquaculture system, its cumulative effect can make or mar the entire system. The amount of dissolved oxygen (DO) alongside other parameters such as temperature, pH, alkalinity and conductivity are often used to estimate the water quality index (WQI) in aquaculture. There exist different approaches for the estimation of the quality index of the water in the aquatic environment. One of such approaches is the use of the Artificial Neural Network (ANN), however, its efficacy lies in the ability to select and use optimal parameters for the network. In this work, different WQI estimation models have been developed using the ANN. These models have been developed by varying the activation function in the hidden layer of the ANN. The performance of the ANN-based estimation models was compared with that of the multilinear regression (MLR) based model. The performance comparison depicts the ANN model case 3 with a tangent activation function as the most accurate and optimal model as compared with MLR model and other ANN models based on the mean square error (MSE), root mean square error (RMSE) and regression (R) metrics. The optimal model has a goodness of fit of 0.998, thereby outweighing other developed models in its capability to estimate the WQI in the aquaculture system
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