Deep Learning-based Management for Wastewater Treatment Plants under Blockchain Environment

Keyi Wan, Zhiwei Guo, Jianhui Wang, Wenru Zeng, Xu Gao, Yu Shen, Keping Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Smart management for sewage treatment plants has always been a hot issue. It is generally implemented on the basis of a data scheduling platform, in which intelligent algorithms can be embedded. The most essential problem for such management is to predict daily business volumes, including amount and quality of wastewater. To achieve a comprehensive perspective, the generation of wastewater is viewed as collaborative effect of multiple factors in social system. This paper proposes a deep learning-based management for sewage treatment plants. Specially, it combines two classical neural network models to construct a hybrid model for precise prediction of business volumes. At last, a set of experiments are carried out to assess the proposed management mechanism. Results reveal that it performs better than general baselines.

Original languageEnglish
Title of host publication2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages106-110
Number of pages5
ISBN (Electronic)9781728187556
DOIs
Publication statusPublished - 2020 Aug
Event2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020 - Chongqing, China
Duration: 2020 Aug 92020 Aug 11

Publication series

Name2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020

Conference

Conference2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020
CountryChina
CityChongqing
Period20/8/920/8/11

Keywords

  • blockchain
  • conventional neural network
  • deep learning
  • long short-term memory
  • prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Health Informatics
  • Instrumentation

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