STUDY ON ABNORMAL WATER QUALITY DETECTION BY DEEP LEARING －FEASIBILITY STUDY AT WASTEWATER TREARMENT FACILITIES

In Japan, water quality accidents caused by oils and chemical substances spilled from factories have occurred. Water quality monitoring is carried out by visual confirmation by facility managers and water quality inspectors at wastewater treatment facilities and sewage treatment plants , but continu...

Full description

Saved in:
Bibliographic Details
Published inArtificial Intelligence and Data Science Vol. 3; no. J2; pp. 231 - 237
Main Authors UTSUNOMIYA, Yutaka, KOMATSU, Masahiro, DOSHO, Noriyuki, UEYAMA, Ko, NAKAMURA, Naoto, YAMAWAKI, Masashi, ISHIKAWA, Yoshihiro, YAMAMOTO, Reiko, MIZUNO, Takafumi
Format Journal Article
LanguageJapanese
Published Japan Society of Civil Engineers 2022
Online AccessGet full text

Cover

More Information
Summary:In Japan, water quality accidents caused by oils and chemical substances spilled from factories have occurred. Water quality monitoring is carried out by visual confirmation by facility managers and water quality inspectors at wastewater treatment facilities and sewage treatment plants , but continuous monitoring is necessary regardless of day and night, and there are problems with the allocation of monitoring personnel and the monitoring method of water quality. Therefore, in this study, we built a model that automatically detects water quality abnormalities using deep learning, which is a type of AI technology and has advanced image analysis ability. Feasibility Study conducted at wastewater treatment facility．As a result, we showed the possibility that deep learning can be an effective technology for improving the sophistication and labor saving of water quality monitoring.
ISSN:2435-9262
DOI:10.11532/jsceiii.3.J2_231