logo
ResearchBunny Logo
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.

00:00
00:00
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting
Engineering and Technology

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting

S. V. Oh, S. Yoo, et al.

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol
Engineering and Technology

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol

T. Hayasaka, A. Lin, et al.

Machine learning assisted dual-functional nanophotonic sensor for organic pollutant detection and degradation in water
Environmental Studies and Forestry

Machine learning assisted dual-functional nanophotonic sensor for organic pollutant detection and degradation in water

J. Zhou, Z. Wu, et al.

Addressing gaps in data on drinking water quality through data integration and machine learning: evidence from Ethiopia
Environmental Studies and Forestry

Addressing gaps in data on drinking water quality through data integration and machine learning: evidence from Ethiopia

A. A. Ambel, R. Bain, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny