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Water Demand Prediction for Housing Apartments Using Time Series Analysis

Author

Listed:
  • Arpit Tripathi

    (SRM Institute of Science and Technology, Chennai, India)

  • Simran Kaur

    (SRM Institute of Science and Technology, Chennai, India)

  • Suresh Sankaranarayanan

    (SRM Institute of Science and Technology, Chennai, India)

  • Lakshmi Kanthan Narayanan

    (SRM Institute of Science and Technology, Chennai, India)

  • Rijo Jackson Tom

    (SRM Institute of Science and Technology, Chennai, India)

Abstract

Water management has always been a topic of serious discussion since infrastructure, rural, and industrial development flourished. Due to the depleting water resources, this is now even a bigger challenge. So, here is developed an IoT-based water management system where ultrasonic sensors are employed for predicting the depth of water in the tank and accordingly pumping the water to the sub tank of the apartment. In addition, the time series analysis Auto Regressive Integrative Moving Average (ARIMA) and Least Square Linear Regression (LSLR) algorithms were employed and compared for predicting the water demand for next six months based on the historical water consumption record of the main reservoir/tank. The information on the amount of water consumed from the main reservoir is pushed to the cloud and to the mobile application developed for utilities. The purpose is to access the water consumption pattern and predict water demand for the next six months from the cloud.

Suggested Citation

  • Arpit Tripathi & Simran Kaur & Suresh Sankaranarayanan & Lakshmi Kanthan Narayanan & Rijo Jackson Tom, 2019. "Water Demand Prediction for Housing Apartments Using Time Series Analysis," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 15(4), pages 57-75, October.
    • Handle: RePEc:igg:jiit00:v:15:y:2019:i:4:p:57-75
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    Cited by:

    1. Xin Liu & Xuefeng Sang & Jiaxuan Chang & Yang Zheng, 2021. "Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4021-4041, September.

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