MODELING DISSOLVED OXYGEN (DO) CONCENTRATION AT KAINJI HYDROPOWER RESERVOIR USING ARTIFICIAL NEURAL NETWORK

Abdulrasaq Apalando Mohammed, Bolaji Fatai Sule, Adebayo Wahab Salami, Adeniyi Ganiyu Adeogun

Abstract


The objective of this study was to develop a multilayer perceptron neural network (MLPNN) and radial basic function neural network (RBFNN) model to predict the dissolved oxygen (DO) at some selected locations at the Kainji hydropower reservoir, Nigeria. The neural networks (NN) model was developed using water quality data collected over a six-year period (2010 to 2015). The NN structure was designed and trained using the SPSS neural network toolbox. The input variables to the NN were: pH, temperature, chloride (Cl-), PO43-, NO3-, Fe2+, and electrical conductivity (EC), while the output was the DO. The performance evaluation of the model was carried out using the coefficient of correlation (r), mean square error (MSE) and mean relative error (MRE). A positive correlation was observed between the actual and simulated DO at the four locations. The results of the simulation showed that the application of the NN and multiple regression analysis to predict DO concentration in water gave satisfactory results for all the selected locations using the two NN modeling approaches. Thus it has been demonstrated that NN modeling tools and multiple regression analysis are very efficient and useful for the computation of water quality parameters.

Keywords


Dissolved oxygen, hydropower reservoir, kainji, neural network and water quality.

Full Text:

PDF

References


Abdolmaleki, A.S. Ahangar, A.G., and Soltani, J. (2013). Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran. Health Scope, 2(1): 31-38.

Abyaneh, H.Z. (2014). Evaluation of Multivariate Linear Regression and Artificial Neural Networks in Prediction of Water Quality Parameters. Journal of Environmental Health Science & Engineering, 12 (40): 1-8.

Ahangar, A.G., Soltani, J., and Abdolmaleki, A.S. (2013). Predicting Mn Concentration in Water Reservoir using Artificial Neural Network (Chahnimeh1 Reservoir, Iran). International Journal of Agriculture and Crop Sciences, 6 (20): 1413-1420.

Areerachakul, S., Sophatsathit, P., and Lursinsap, C. (2013). Integration of Unsupervised and Supervised Neural Networks to Predict Dissolved Oxygen Concentration in Canals. Ecological Modelling, 261-262: 1-7.

Ay, M. and Kisi, O. (2013). Modelling COD Concentration by Using Different Artificial Intelligence Methods Digital Proceedings of ICOEST, Cappadocia Nevsehirs, Turkey, pp. 477-489.

Baskaran, T., Nagan, S., and Rajamohan, S. (2010). Inflow and Sediment Yield Modeling for Reservoirs Using Artificial Neural Network. International Journal of Earth Sciences and Engineering, 3(3): 382-388.

Brosse, S., Guegan, J., Tourenq, J., and Lek, S. (1999). The Use of Artificial Neural Networks to Assess Fish Abundance and Spatial Occupancy in the Littoral Zone of a Mesotrophic Lake. Ecological Modelling, Elsevier, 120: 299-311.

Dedecker, A.P., Goethals, P.L.M., Gabriels, W., and Pauw, N.D. (2004). Optimization of Artificial Neural Network (ANN) Model Design for Prediction of Macroinvertebrates in the Zwalm River Basin (Flanders, Belgium). Ecological Modelling, 174: 161-173.

Giri, A. and Singh, N.B. (2014). Comparison of Artificial Neural Network Algorithm for Water Quality Prediction of River Ganga. Environmental Research Journal, 8(2): 55-63.

Heydari, M., Olyaie, E., Mohebzadeh, H., and Kisi, Ö. (2013). Development of a Neural Network Technique for Prediction of Water Quality Parameters in the Delaware River, Pennsylvania. Middle-East Journal of Scientific Research, 13 (10): 1367-1376.

Jeong, K.S., Kim, D.K., and Joo, G.J. (2006). River Phytoplankton Prediction Model by Artificial Neural Network: Model Performance and Selection of Input Variables to Predict Time-Series Phytoplankton Proliferations in a Regulated River System. Ecological Informatics, 1, 235-245.

Jørgensen, S.E., Costanza, R., and Xu, F. (2005). Handbook of Ecological Indicators for Assessment of Ecosystem Health, USA, CRC Press, Taylor and Francis Group.

Kişi, O., and Ay, M. (2011). Modeling Dissolved Oxygen (DO) Concentration Using Different Neural Network Techniques. International Balkans Conference on Challenges of Civil Engineering, Epoka University, Tirana, Albania. pp. 1-7.

Lae, R., Lek, S., and Moreau, J. (1999). Predicting Fish Yield of African Lakes using Neural Networks.Ecological Modelling, Elsevier, 120, 325-335.

Merdun, H., and Cinar, O. (2010). Artificial Neural Network and Regression Techniques in Modelling Surface Water Quality. Environment Protection Engineering, 36, (2): 95-109.

Mihajlović, I., Nikolić, D., Štrbac, N., and Živković, Z. (2010). Statistical Modelling in Ecological Management Using the Artificial Neural Networks (ANNs). Serbian Journal of Management, 5(1): 39-50.

Moatar, F., Fessant, F., and Poire, A. (1999). pH Modelling by Neural Networks: Application of Control and Validation Data Series in the Middle Loire River. Ecological Modelling, Elsevier, 120, 141-156.

Możejko, J., and Gniot, R. (2008). Application of Neural Networks for the Prediction of Total Phosphorus Concentrations in Surface Waters. Polish J. of Environ. Stud., 17, (3): 363-368.

Neto, B.S.R., Hauser-Davis, R.A., Lobato, T.C., Saraiva, A.C.F., Brandão, I.L.S., Oliveira, T.F.O., and Silveira A. M. (2014). Estimating Physicochemical Parameters and Metal Concentrations in Hydroelectric Reservoirs by Virtual Sensors: A Case Study in the Amazon Region. Computer Science and Engineering, 4(2): 43-53.

Palani, S., Liong, S.Y., and Tkalich, P. (2008). An ANN Application for Water Quality Forecasting. Marine Pollution. Bull., 56, 1586-1597.

Radojevic, I., Comic, L., Rankovic, V., Ostojic, A., and Topuzovic, M. (2013). Applying Neural Networks for Predicting the Facultative Oligotrophic Bacteria in Two Reservoirs with Different Trophic State. Journal of Environmental Protection and Ecology, 14(1): 55-63.

Rak, A. (2013). Water Turbidity Modelling During Water Treatment Processes Using Artificial Neural Networks. International Journal of Water Sciences, 2(3): 1-10.

Rankovi´c, V., Radulovi´c, J., Radojevi´c, I., Ostoji´c, A., and ˇComi´c, L. (2010). Neural Network Modeling of Dissolved Oxygen in the Gruˇza Reservoir, Serbia. Ecological Modeling, Elsevier, 221: 1239-1244.

Sivri, N., Ozcan, H.K., Ucan, O.N., and Akincilar, O. (2009). Estimation of Stream Temperature in Degirmendere River, Trabzon-Turkey Using Artificial Neural Network Model. Turkish Journal of Fisheries and Aquatic Sciences, 9, 145-150.

Vicente, H., Couto, C., Machado, J., Abelha, A. and Neves, J. (2012). Prediction of Water Quality Parameters in a Reservoir Using Artificial Neural Networks. International Journal of Design & Nature and Ecodynamics, 7(9): 310-319.




DOI: https://doi.org/10.11113/mjce.v30n3.521

Refbacks

  • There are currently no refbacks.


Copyright © 2018 Penerbit UTM Press, Universiti Teknologi Malaysia.
Disclaimer : This website has been updated to the best of our knowledge to be accurate. However, Universiti Teknologi Malaysia shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.
Best viewed: Mozilla Firefox 4.0 & Google Chrome at 1024 × 768 resolution.