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
Highlights
Stream pollution is any impairment to the native water characteristics through the addition of anthropogenic contaminants to the extent that it is no more useful for drinking purposes or support the biotic communities living on it (Agrawal et al, 2010)
The three layer feed-forward model with back-propagation multi-layer perceptron (MLP) type of neural network with the architecture of 15-9-1 for biochemical oxygen demand (BOD) and 15-13-1 for OD as input, hidden and output units respectively was used for the duration of the study
The selection of months of June to December, 2014 and January, 2015 as peak periods of raining and dry season was to capture some activities like flood and refuse dumping into river that normally resulted into river water pollution
Summary
Stream pollution is any impairment to the native water characteristics through the addition of anthropogenic contaminants to the extent that it is no more useful for drinking purposes or support the biotic communities living on it (Agrawal et al, 2010). If predicted quality is not satisfying, some changes or precaution measures must be implemented. To prevent this unwanted trend, control of water pollution seriously has become very essential to maintain the sustainability of water resources. The objectives of this study were to use the ANN to develop models for Asa River water pollution in Ilorin, Kwara State, to find the best neural network architecture for the process artificial model in the prediction of Asa River water pollution and to evaluate the performance of the process of ANN models after the elimination of some less significant input parameters through stepwise regression analysis
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