{"title":"Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a Python framework approach","authors":"Punit Khatri, K. K. Gupta, Raj Kumar Gupta","doi":"10.5194/DWES-12-31-2019","DOIUrl":null,"url":null,"abstract":"Abstract. This paper proposes the development of a Raspberry Pi-based\nhardware platform for drinking-water quality monitoring. The selection of\nwater quality parameters was made based on guidelines of the Central\nPollution and Control Board (CPCB), New Delhi, India. A graphical user interface\n(GUI) was developed for providing an interactive human machine interface to\nthe end user for ease of operation. The Python programming language was used for\nGUI development, data acquisition, and data analysis. Fuzzy computing\ntechniques were employed for decision-making to categorize the water quality\nin different classes like “bad”, “poor”, “satisfactory”, “good”, and\n“excellent”. The system has been tested for various water samples from eight\ndifferent locations, and the water quality was observed as being good,\nsatisfactory, and poor for the measured water samples. Finally, the\nobtained results were compared with the benchmark for authentication.\n","PeriodicalId":53581,"journal":{"name":"Drinking Water Engineering and Science","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drinking Water Engineering and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/DWES-12-31-2019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 20
Abstract
Abstract. This paper proposes the development of a Raspberry Pi-based
hardware platform for drinking-water quality monitoring. The selection of
water quality parameters was made based on guidelines of the Central
Pollution and Control Board (CPCB), New Delhi, India. A graphical user interface
(GUI) was developed for providing an interactive human machine interface to
the end user for ease of operation. The Python programming language was used for
GUI development, data acquisition, and data analysis. Fuzzy computing
techniques were employed for decision-making to categorize the water quality
in different classes like “bad”, “poor”, “satisfactory”, “good”, and
“excellent”. The system has been tested for various water samples from eight
different locations, and the water quality was observed as being good,
satisfactory, and poor for the measured water samples. Finally, the
obtained results were compared with the benchmark for authentication.
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