Prediction of Influent Flow Rate: Data-Mining Approach

Abstract

In this paper, models for short-term prediction of influent flow rate in a wastewater-treatment plant are discussed. The prediction horizon of the model is up to 180 min. The influent flow rate, rainfall rate, and radar reflectivity data are used to build the prediction model by different data-mining algorithms. The multilayer perceptron neural network algorithm has been selected to build the prediction models for different time horizons. The computational results show that the prediction model performs well for horizons up to 150 min. Both the peak values and the trends are accurately predicted by the model. There is a small lag between the predicted and observed influent flow rate for horizons exceeding 30 min. The lag becomes larger with the increase of the prediction horizon. DOI: 10.1061/(ASCE)EY.1943-7897 .0000103. © 2013 American Society of Civil Engineers. CE Database subject headings: Data collection; Algorithms; Flow rates; Neural networks; Radar; Rainfall; Wastewater management; Water treatment plants; Predictions. Author keywords: Data-mining algorithms; Influent flow rate; Multilayer perceptron neural networks; Radar reflectivity; Rainfall; Wastewater-treatment plant.