Artificial Neural Network Modelling of Biochemical Oxygen Demand and Dissolved Oxygen of Rivers: Case Study of Asa River

Kamoru Akanni Adeniran, Bashir Adelodun, Matthew Ogunshina

Abstract


Water quality assessment involves the determination of a number of parameters using several analytical methods which are often tedious and time consuming. Artificial Neural Network (ANN) was used in this study to model the relationship between fifteen (15) water quality parameters used to predict other two (2) related parameters in other to reduce the burden of long experimental procedures. Water samples were collected from six (6) point and non point sources of pollution along Asa River in Ilorin during the peak of rainy season (June–Aug, 2014) and peak of dry season (Nov–Jan, 2015). Physical and chemical parameters inputted into the models include pH, turbidity, total dissolved solids, temperature, electrical conductivity, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, hardness, chloride, sulphate, phosphate, calcium, magnesium and nitrate. The output models include: biochemical oxygen demand (BOD) and dissolved oxygen (DO).  The three layer feed-forward model with back-propagation multi-layer perception (MLP) models architecture of 15-9-1 for BOD and 15-13-1 for DO yielded optimal results with 9 and 13 neurons in hidden layer for BOD and DO respectively. The ANN was successfully trained and validated with 83% and 17% of the data sets respectively. Performance of the models was evaluated by statistical criteria of average error (AE) and mean square error (MSE). The correlation coefficients of ANN models for prediction of BOD and DO were 0.9525 and 0.9556 respectively. Sensitivity analysis was also carried out to identify the most significant input-output relationship. Hence, the ANNs was able to show remarkable prediction performance to predicting the BOD and DO in Asa River, Ilorin.

DOI: http://dx.doi.org/10.5755/j01.erem.72.3.14120


Keywords


Artificial Neural Network model, Asa river and water quality parameters

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Print ISSN: 1392-1649
Online ISSN: 2029-2139