Study of Machine Learning Algorithms for water meter
T Charumathi
, A.Nasreen Banu , B.Pavithra , Dr.M.Kiruthuga devi
Keywords—Water Consumption, Big Data Management, Sensor Network,Android based application,Monitoring,Smart water meter, Usage prediction , Billing.
ABSTRACT--Water conservation is big issue in many apartments. Apartment association should take initiative to send the message of the amount of water consumed to all residents. There are many smart water meter exists to predict the consumption of water. But we hardly use it. There are number of people who have issues in prediction. These systems can predict in real time but they fail to give accurate usage report and billing error. There are various machine learning algorithms such as Linear Regression, Decision Tree, K-Nearest Neighour, Random Forest. Etc. The prediction method is done by the most popular and the best machine learning model and algorithm is Linear Regression. Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. It’s one of the most well- known and well-understood algorithms in statistics and machine learning. Predictive modelling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explain ability. The Mobile application is developed for smart water meter of pervasiveness (anytime anywhere) to control the water consumption. Smart meter for water utilization provides solution for water issues and it measures the quantity of water consumed by each household and allow the user to monitor the consumption level. While installing this smart water meter we should keep track on the water consumed over the internet. The supply of water can be ended if the residents are not present in their home and it reduces energy consumption directly or indirectly.
"Study of Machine Learning Algorithms for water meter ", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.c293-c318, March-2023, Available :https://ijnrd.org/papers/IJNRD2303241.pdf
Volume 8
Issue 3,
March-2023
Pages : c293-c318
Paper Reg. ID: IJNRD_188867
Published Paper Id: IJNRD2303241
Downloads: 000118852
Research Area: Information Technology
Country: Chennai , Tamilnadu , India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
