Data-Driven Analysis and Prediction using Regression Models on Iot Based Drainage Monitoring System
Telugu Maddileti1, Anthony David Victor Raj2, Vinayak Neemkar3, R.K.Pongiannan4
1Telugu Maddileti, Assistant Professor, Electronics and Computers Department, Sreenidhi Institute of Science and Technology, HYD, Telangana.
2Anthony David Victor Raj, Electronics and Computers Department, Sreenidhi Institute of Science and Technology, HYD, Telangana.
3Vinayak Neemkar, Electronics and Computers Department, Sreenidhi Institute of Science and Technology, HYD, Telangana.
4R.K. Pongiannan, Professor, Department of EEE, SRM Institute of Science and Technology, Kattankulathur, (Tamil Nadu), India.
Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 246-250 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36151081219/2019©BEIESP | DOI: 10.35940/ijitee.L3615.1081219
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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: India with an ambitious goal has stated of coming up with a project of making over a hundred smart cities. The government needs to take several key parameters into consideration few of which are intelligent water systems, intelligent electrical systems, intelligent transport, smart homes etc. The intention of this paper is to come up with a solution using modern technologies of IOT and Machine learning to get a detailed exploration of the data collected through various IOT sensors. The data is processed and used for training the Machine learning models which help in further predicting the safety of future drain data by recognizing patterns and gathering insights using visualizations. Thus, helping to identify and analyze problems related to drains in a more efficient and optimized manner.
Keywords: IoT, Machine learning, Sensors, Processing, Smart, Safety.
Scope of the Article: Machine learning