River Water Assessment and Prediction Modelling
Visnu Darshini1, Manu Satwik2, Prudhvi3, Harshith4
1Visnu Darshini*, Department of CSE, SRM University, Chennai, India. Manu Satwik, Department of CSE, SRM University, Chennai, India.
2Prudhvi, Department of CSE, SRM University, Chennai, India.
3Harshith Suresh, Department of CSE, SRM University, Chennai, India.
Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 1768-1770 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8076049420/2020©BEIESP | DOI: 10.35940/ijeat.D8076.049420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: This is a wireless based water quality checking system. It is a WSN which includes a raspberry pie for processing the system and various sensors like pH, turbidity, temperature, conductivity. Internet of things acts as a query to response service and remote communication technology in sending and receiving the data. Using k-means clustering a machine learning algorithm which helps in measuring large amounts of data and comparing the data with the standard values. Any fluctuations in the data with the recorded values will notify the system. The information is monitored constantly and consigned to a cloud. Therefore this system immensely helps in assessing the quality of contaminated water with prediction modelling.
Keywords: Internet of things, k-means clustering, assessing, prediction modelling, wireless network.