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Due to the actual situation of climate change, population growth, and consequent water scarcity, especially in urban areas, experts agree on the need for more efficient resource management based on prediction and anticipation. In this context, machine learning algorithms can play a key role in modeling the behavior of users that are part of a water grid. As first steps in this direction, this work presents an analysis of the dynamics of water consumption throughout an entire year based on dimensionality reduction and clustering techniques.

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Monitoring the Dynamics of Water Consumption from Time Series Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Manuel Rubiños,
  • Agustín García-Fischer,
  • Noel Freire-Mahía,
  • Héctor Quintián

摘要

Due to the actual situation of climate change, population growth, and consequent water scarcity, especially in urban areas, experts agree on the need for more efficient resource management based on prediction and anticipation. In this context, machine learning algorithms can play a key role in modeling the behavior of users that are part of a water grid. As first steps in this direction, this work presents an analysis of the dynamics of water consumption throughout an entire year based on dimensionality reduction and clustering techniques.