Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a Python framework approach

Author:

Khatri PunitORCID,Gupta Karunesh KumarORCID,Gupta Raj KumarORCID

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.

Publisher

Copernicus GmbH

Subject

Pollution,Water Science and Technology,Civil and Structural Engineering

Reference30 articles.

1. Alkandari, A. A. and Moein, S.: Implementation of Monitoring System for Air Quality using Raspberry PI: Experimental Study, Indones. J. Elec. Eng. Comput. Sci., 10, 43–49, https://doi.org/10.11591/ijeecs.v10.i1.pp43-49, 2018.

2. Anan, K.: `Water-Related Diseases Responsible For 80 Per Cent of All Illnesses, Deaths In Developing World', Says Secretary-General In Environment Day Message, UN, 1, available at: http://www.un.org/press/en/2003/sgsm8707.doc.htm (last access: 6 February 2018), 2003.

3. Anilkumar, B. and Srikanth, K. R. J.: Design and development of real time paper currency recognition system of demonetization New Indian Notes by using raspberry Pi for visually challenged, Int. J. Mech. Eng. Technol., 9, 884–891, 2018.

4. Anon: SciKit-Fuzzy – skfuzzy v0.2 docs, available at: http://pythonhosted.org/scikit-fuzzy/overview.html, last access: 20 March 2018.

5. Bernabé, G., Hernández, R., and Acacio, M. E.: Parallel implementations of the 3D fast wavelet transform on a Raspberry Pi 2 cluster, J. Supercomput., 74, 1765–1778, https://doi.org/10.1007/s11227-016-1933-2, 2018.

Cited by 20 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Computational Methods For Water Quality Index Calculation Using Real-Time Measurement System;2024 13th Mediterranean Conference on Embedded Computing (MECO);2024-06-11

2. Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation;International Journal of Engineering;2024

3. AquaNet: A Quality Monitoring System for Rural Potable Water Distribution Scheme Using Smart Things;Springer Proceedings in Earth and Environmental Sciences;2024

4. Leveraging Artificial Intelligent Model for Water Quality Indices Assessment: A Comprehensive Study and Framework;2023 International Conference on the Cognitive Computing and Complex Data (ICCD);2023-10-21

5. IoT System for Real-Time Water Quality Measurement and Data Visualization;2023 12th Mediterranean Conference on Embedded Computing (MECO);2023-06-06

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3