Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things
Jaihind G1, Ezhilarasie R2, Umamakeswari A3
1Jaihind G, Department of Embedded Systems, SASTRA University, Thanjavur (Tamil Nadu), India.
2Ezhilarasie R, Department of CSE, SASTRA University, Thanjavur (Tamil Nadu), India.
3Umamakeswari A, Department of CSE, SASTRA University, Thanjavur (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 53-57 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10140785S319/19©BEIESP | DOI: 10.35940/ijeat.E1014.0785S319
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© 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: IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.
Keywords: Internet of Things (IoT), Machine Learning Techniques, Random Forest, Water Quality, WEKA.
Scope of the Article: Internet of Things