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Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

Content Provider	Springer Nature Link
Author	Kim, Minsoo Kim, Yejin Kim, Hyosoo Piao, Wenhua Kim, Changwon
Copyright Year	2016
Abstract	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.
Starting Page	299
Ending Page	310
Page Count	12
File Format	PDF
ISSN	20952201
Journal	Frontiers of Environmental Science & Engineering in China
Volume Number	10
Issue Number	2
e-ISSN	2095221X
Language	English
Publisher	Higher Education Press
Publisher Date	2015-12-14
Publisher Institution	Chinese Universities
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	influent wastewater prediction data-driven model k-nearest neighbor method (k-NN) Environment
Content Type	Text
Resource Type	Article
Subject	Environmental Science

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