Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Mawson Lakes, Adelaide, SA, 5095, Australia.
Future Industries Institute, University of South Australia, Adelaide, SA, 5095, Australia.
Environ Sci Pollut Res Int. 2022 Oct;29(47):70984-70999. doi: 10.1007/s11356-022-20777-y. Epub 2022 May 20.
Forecasts of wastewater inflow are considered as a significant component to support the development of a real-time control (RTC) system for a wastewater pumping network and to achieve optimal operations. This paper aims to investigate patterns of the wastewater inflow behaviour and develop a seasonal autoregressive integrated moving average (SARIMA) forecasting model at low temporal resolution (hourly) for a short-term period of 7 days for a real network in South Australia, the Murray Bridge wastewater network/wastewater treatment plant (WWTP). Historical wastewater inflow data collected for a 32-month period (May 2016 to December 2018) was pre-processed (transformed into an hourly dataset) and then separated into two parts for training (80%) and testing (20%). Results reveal that there is seasonality presence in the wastewater inflow time series data, as it is heavily dependent on time of the day and day of the week. Besides, the SARIMA (1,0,3)(2,1,2) was found as the best model to predict wastewater inflow and its forecasting accuracy was determined based on the evaluation criteria including the root mean square error (RMSE = 5.508), the mean absolute value percent error (MAPE = 20.78%) and the coefficient of determination (R = 0.773). From the results, this model can provide wastewater operators curial information that supports decision making more effectively for their daily tasks on operating their systems in real-time.
污水流量预测被认为是支持污水管网实时控制系统开发和实现最佳运行的重要组成部分。本文旨在研究污水流入行为模式，并为南澳大利亚默里桥污水管网/污水处理厂（WWTP）的实际网络开发一个短期（7 天）、低时间分辨率（每小时）的季节性自回归综合移动平均（SARIMA）预测模型。历史污水流入数据收集了 32 个月（2016 年 5 月至 2018 年 12 月），经过预处理（转换为每小时数据集），然后分为两部分用于训练（80%）和测试（20%）。结果表明，污水流入时间序列数据存在季节性，因为它严重依赖于一天中的时间和星期几。此外，SARIMA（1,0,3）（2,1,2）被发现是预测污水流入的最佳模型，其预测精度是基于评价标准确定的，包括均方根误差（RMSE=5.508）、平均绝对百分比误差（MAPE=20.78%）和确定系数（R=0.773）。从结果来看，该模型可以为污水操作人员提供关键信息，以便更有效地支持他们实时操作系统的日常任务决策。
