In the training process, eight indicators are set as inputs and the water quality grade is set as the output. Since sometimes the output of WNN is not an integer, it means the output of the WNN model cannot be used for the assessment grade directly. We define water quality grade based on the following rule: when the predicted value is less than 1.5, the sample is identified as Grade I. When the predicted value is greater than 1.5 and less than 2.5, the sample is identified as Grade II, and the rest is done in the same manner. Table 4 summarizes these results. Based on the convergence of training error and the matching degree of testing results, the nodes in the hidden layer is determined as 23. After the 2000th iteration, the target error reaches 0.056, and the WNN training process is finished. Figure 7 illustrates the comparison between the evaluated and the actual water quality grade. Obviously the trained WNN model has a higher accuracy by comparing the estimated results with the actual water quality grade. So it is feasible to apply the WNN model for assessing water quality.