Prediction of Water Demand for Domestic Purpose Using Multiple Linear Regression

B. N. Chandrashekar Murthy (), H. N. Balachandra, K. Sanjay Nayak and C. Chakradhar Reddy
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B. N. Chandrashekar Murthy: JSS Science and Technology University, Sri Jayachamarajendra College of Engineering, Department of Electronics and Communication
H. N. Balachandra: JSS Science and Technology University, Sri Jayachamarajendra College of Engineering, Department of Electronics and Communication
K. Sanjay Nayak: JSS Science and Technology University, Sri Jayachamarajendra College of Engineering, Department of Electronics and Communication
C. Chakradhar Reddy: JSS Science and Technology University, Sri Jayachamarajendra College of Engineering, Department of Electronics and Communication

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 811-817 from Springer

Abstract: Abstract Water is the key for life to sustain and to guarantees people’s quality of life. The water resource management plays an important role in checking the unnecessary wastage of water. In water resource management the demand forecasting plays the key feature in the planning of the distribution of water. There are traditional methods for forecasting the demand, but these methods lack in accuracy. To predict the demand it is possible to use the multiple linear regression, which offers more accuracy than the traditional method. In this paper, we propose a system which uses a ultrasonic sensor, NodeMCU and a progressive web app to collect the water usage data from the user. The NodeMCU and the ultrasonic sensor constitutes the hardware of the system which is a IoT Level 3 system. Using the collected data with multiple linear regression it is possible to predict the water required for the next month for the user. The proposed system was evaluated for 10 data sets and the results were comparable.

Keywords: Multiple linear regression; Web progressive app; Water resource management; IoT level 3 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_81

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DOI: 10.1007/978-3-030-41862-5_81

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