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Author:

Wang, Gongming (Wang, Gongming.) | Chen, Hong (Chen, Hong.) | Han, Honggui (Han, Honggui.) | Bi, Jing (Bi, Jing.) | Qiao, Junfei (Qiao, Junfei.) | Tirkolaee, Erfan Babaee (Tirkolaee, Erfan Babaee.)

Indexed by:

EI Scopus SCIE

Abstract:

Water quality prediction is an indispensable task in water environment and source management. The existing predictive models are mainly designed by data-driven artificial neural networks (ANNs), especially deep learning models for large-scale water quality prediction. However, the state of water environment is a dynamic process where the stationarity of water quality data suffers from time variation and human activities, which leads to a poor prediction accuracy because ANNs receive whole water quality data passively, including abnormal conditions. We consider such a tough problem in this article and propose an event-triggered deep fuzzy neural network (ET-DFNN) to pursue the better performance of water quality prediction in the complex water environment. First, a deep pretraining model is constructed to extract the effective features from raw water quality data. Second, we construct a DFNN model where the extracted effective features are considered as the input variables. Third, some events are defined to characterize the abnormal conditions of state evolution in water quality. The DFNN is trained and updated using different learning strategies only when the corresponding events are triggered, otherwise it ignores the current data sample and directly goes to the next data sample. The practical data-based experimental results show that the ET-DFNN achieves better prediction performance in accuracy and efficiency than its peers. Especially, the training efficiency of ET-DFNN is improved by 57.94% on total phosphorus prediction and 48.31% on biochemical oxygen demand prediction, respectively.

Keyword:

Water resources water environment Predictive models Fuzzy neural networks Deep fuzzy neural network (DFNN) Artificial neural networks Water quality event-triggered strategy Training water quality prediction Feature extraction nonstationarity

Author Community:

  • [ 1 ] [Wang, Gongming]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing Lab Smart Environm Protect, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
  • [ 2 ] [Han, Honggui]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing Lab Smart Environm Protect, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
  • [ 3 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing Lab Smart Environm Protect, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China
  • [ 4 ] [Chen, Hong]Lvzhiyuan Environm Grp, Rizhao 276801, Peoples R China
  • [ 5 ] [Bi, Jing]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Tirkolaee, Erfan Babaee]Istinye Univ, Dept Ind Engn, TR-34396 Istanbul, Turkiye
  • [ 7 ] [Tirkolaee, Erfan Babaee]Yuan Ze Univ, Dept Ind Engn & Management, Taoyuan City 320315, Taiwan
  • [ 8 ] [Tirkolaee, Erfan Babaee]Lebanese Amer Univ, Dept Ind & Mech Engn, Byblos 36, Lebanon

Reprint Author's Address:

  • [Qiao, Junfei]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing Lab Smart Environm Protect, Beijing Key Lab Computat Intelligence & Intelligen, Beijing 100124, Peoples R China;;

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Source :

IEEE TRANSACTIONS ON FUZZY SYSTEMS

ISSN: 1063-6706

Year: 2024

Issue: 5

Volume: 32

Page: 2690-2699

1 1 . 9 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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