Predicting water quality in municipal water management systems using a hybrid deep learning model

Increasing municipal waste generation puts more and more municipal water resources at high risk. Accurate prediction of water quality becomes critical for effective protection of the water resources. Due to the nonlinear and non-stationary characteristics of water quality data of the municipal water...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Engineering applications of artificial intelligence Ročník 133; s. 108420
Hlavní autori:	Luo, Wenxian, Huang, Leijun, Shu, Jiabin, Feng, Hailin, Guo, Wenjie, Xia, Kai, Fang, Kai, Wang, Wei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.07.2024
Predmet:	Attention mechanism Convolutional neural network Encoder–decoder structure Long short-term memory Multi-step prediction Water quality Long short-term memory Attention mechanism Multi-step prediction Convolutional neural network Encoder–decoder structure Water quality
ISSN:	0952-1976, 1873-6769
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	Increasing municipal waste generation puts more and more municipal water resources at high risk. Accurate prediction of water quality becomes critical for effective protection of the water resources. Due to the nonlinear and non-stationary characteristics of water quality data of the municipal water resources, it is challenging to achieve high prediction accuracy, especially for medium-term and long-term predictions. To address this issue, we propose a novel hybrid deep learning model to predict water quality multiple steps ahead. The proposed model adopts the encoder–decoder structure in the form of two long short-term memory (LSTM) networks, integrated with the attention mechanism and a convolutional neural network (CNN). The model extracts the complex correlation between multiple water quality features through the CNN, and uses the two LSTM networks to transfer historical information to predictions, with an attention layer assigning different weights to the different parts of the historical information. Using three years of water quality data collected from an urban river, we experimentally show that the proposed model outperforms the baseline models by 11%–34% in root mean squared error (RMSE) when predicting dissolved oxygen multiple steps ahead, and by 1%–7% when predicting total phosphorus. Similar improvement has also been found in Nash–Sutcliffeefficiency (NSE) and mean absolute error (MAE). The proposed model is a feasible solution for multi-step medium-term water quality prediction.
ISSN:	0952-1976 1873-6769
DOI:	10.1016/j.engappai.2024.108420