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Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

Environmental Studies and Forestry

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

A. T. Abraha, T. A. Woldeamanuel, et al.

This intriguing study, conducted by Abraha Tesfay Abraha, Tibebu Assefa Woldeamanuel, and Ephrem Gebremariam Beyene, delves into residential water consumption in Adama city, Ethiopia. It uncovers significant variances in water use across urban areas, emphasizing the urgent need for sustainable water management practices and conservation efforts.

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Playback language: English
Abstract
This study investigates residential water consumption in Adama city, Ethiopia, to inform water-sensitive interventions. Using a combination of top-down and bottom-up data collection, and integrating machine learning with spatial and socioeconomic analyses, the study identifies key factors influencing water consumption and predicts daily household water use. Findings reveal significant variations in consumption across urban areas, highlighting the need for improved water conservation habits and sustainable water management practices.
Publisher
npj Clean Water
Published On
Apr 09, 2024
Authors
Abraha Tesfay Abraha, Tibebu Assefa Woldeamanuel, Ephrem Gebremariam Beyene
Tags
residential water consumption
Ethiopia
Adama city
sustainable water management
machine learning
socioeconomic analysis

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