Iot Based Water Quality Monitoring System using Machine Learning
P.Baskaran1, D. Selva Pandiyan2, D.Jebakumar Immanuel3, R.M. Bhavadharini4
1P.Baskaran*, Assistant Professor, Department of CSE, Easwari Engineering College, Chennai, India.
2D.Selva Pandian, Assistant Professor, Department of CSE, Karpagam Academy of Higher Education, Coimbatore, India.
3Dr. Jebakumar Immanuel. D, Assistant Professor, Department of CSE, SNS College of Engineering, Coimbatore, India.
4Dr.R. M. Bhavadharini, Associate Professor, Department of CSE, Easwari Engineering College, Chennai, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 11801-11805 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9196118419/2019©BEIESP | DOI: 10.35940/ijrte.D9196.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the present occasions, because of urbanization and contamination, it has gotten important to screen and assess the nature of water arriving at our homes. Guaranteeing safe inventory of drinking water has become a major test for the cutting edge progress. In this desk work, we present a structure and improvement of a minimal effort framework for continuous checking of the water quality (WQ) in IoT (web of things). The framework comprise of a few sensors are accustomed to guesstimatingsomatic and element limitations of the water. The parameters like temperature, PH, turbidity, conductivity, broke up oxygen of the water can be estimated. The deliberate qualities from the sensors can be prepared by the center controller. The RBPI B+ (RBPI) model can be consumed as a center controller. At last, the instrument facts can be understood on web utilizing distributed computing. Here the information’s are handled utilizing AI calculation it sense the water condition if the WQis great it open the entryway divider else it shuts the door divider. This whole procedure happens naturally without human mediation therefore spare an opportunity to contract with the circumstance physically. The uniqueness of our proposed research is to get the water observing framework with high recurrence, high portability, and low controlled.
Keywords: Cloud Computing, Internet of Things, RBPI, Turbidity, Wireless Sensor Networks.
Scope of the Article: Cloud Computing.