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

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 Environmental Research, Engineering and Management 2016/72/3 60 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.

Investigating the Influence of Column Depth on the Treatment of Textile Wastewater Using Natural Zeolite

“…The high negative correlation between BOD and DO can be linked to the fact that BOD directly affects the amount of dissolved oxygen in the water, which means the greater the BOD, the more rapidly oxygen is depleted [43].…”

Regulation of Color, pH, and Biochemical Oxygen Demand of Asa River Water Using a Luffa cylindrica Biomass Packed Bed

“…In addition to agricultural waste and other practices that lead to its contamination, it has also been reported that Asa River's major identified source of pollution was the direct runoff of industrial effluents from factories situated along its path [2]. Adeniran et al [3] reported that Asa River is subject to a high level of eutrophication due to the organic matter and industrial effluents discharged into it.…”