发表机构
Univ Paris Est Creteil; Univ Gustave Eiffel; CNRS（巴黎东克雷泰伊大学; 古斯塔夫·埃菲尔大学; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用多变量LSTM模型预测污水水质，基于高频时间序列数据，在24小时预测任务中优于持久性模型和SARIMA模型，尤其对高噪声非线性变量表现更佳。
AI 中文摘要
所提出的方法依赖于在Seine aval（SIAAP 1）污水处理厂收集的高频多变量时间序列，包括pH值、温度、电导率和总悬浮固体，以及与降水和上游工厂测量相关的外生变量。目标是预测24小时范围内的污水水质。实现了一个多变量长短期记忆（LSTM）循环神经网络，以捕捉数据中复杂的时间依赖性和非线性模式。该模型在一年数据上进行训练，并在四个月的数据（每个季节一个月）上进行验证，并与持久性模型和SARIMA模型进行比较。评估显示LSTM模型总体优越，特别是对于表现出高噪声和非线性水平的变量。
英文摘要
The proposed approach relies on high-frequency multivariate time series collected at the Seine aval (SIAAP 1 ) treatment plant, including pH, temperature, conductivity, and total suspended solids, as well as exogenous variables related to precipitation and measurements from an upstream plant. The objective is to forecast wastewater quality over a 24-hour horizon. A multivariate Long Short-Term Memory (LSTM) recurrent neural network is implemented to capture complex temporal dependencies and nonlinear patterns in the data. The model is trained on one year of data and validated on four months of data (one per season), and is compared with a persistence model and SARIMA models. The evaluation shows an overall superiority of the LSTM model, particularly for variables exhibiting high levels of noise and nonlinearity.
