The k-nearest neighbor （k-NN） method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand （COD）, suspended solid （SS）, total nitrogen （T-N） and total phosphor...The k-nearest neighbor （k-NN） method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand （COD）, suspended solid （SS）, total nitrogen （T-N） and total phosphorus （T-P） at a wastewater treatment plant （WWTP）. The search range and approach for determining the number of nearest neighbors （NNs） under dry and wet weather conditions were initially optimized based on the root mean square error （RMSE）. The optimum search range for considering data size was one year. The square root-based （SR） approach was superior to the distance factor-based （DF） approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error （MAPE） under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.展开更多
文摘The k-nearest neighbor （k-NN） method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand （COD）, suspended solid （SS）, total nitrogen （T-N） and total phosphorus （T-P） at a wastewater treatment plant （WWTP）. The search range and approach for determining the number of nearest neighbors （NNs） under dry and wet weather conditions were initially optimized based on the root mean square error （RMSE）. The optimum search range for considering data size was one year. The square root-based （SR） approach was superior to the distance factor-based （DF） approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error （MAPE） under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.
