<![CDATA[The issue of drinking water quality and its suitability for human consumption represents a significant concern in contemporary society, particularly in the context of maintaining public health. The existing research on the classification of drinking water eligibility has yet to yield conclusive results. The objective of this research is to utilize the backpropagation neural network method to categorize drinking water feasibility data, thereby ensuring that the water consumed meets established safety standards. The data utilized in this study were obtained from an open repository and encompass a total of 3,276 data points. The data set comprises nine water quality parameter attributes, namely pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The data underwent a series of pre-processing steps, including the removal of missing values, the replacement of missing values with the average value of the attribute, and normalization using the MinMax Scaler and Z-score methods. The artificial neural network architecture comprises three principal components: input, hidden, and output neurons. The optimal architecture scenario is [9; 17; 15; 10; 1], comprising nine input neurons, 17 neurons in the initial hidden layer, 15 neurons in the second hidden layer, 10 neurons in the third hidden layer, and a single output neuron. The evaluation results demonstrate that this model effectively classifies drinking water eligibility data with an accuracy rate of 0.6579. However, the results indicate that the accuracy achieved requires further improvement for more reliable applications. These findings illustrate the promising potential of the BPNN method in classifying drinking water quality data.]]>
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