Estimating household water storage from images: A machine learning approach
Year: 2025
Type: article
Abstract: ABSTRACT Whilst much research focusses on challenges related to achieving SDG 6.1 (universal and equitable access to safe and affordable drinking water), there has been less attention to challenges of safe transport, storage and use of collected water. In particular, there are relatively few high-quality datasets quantifying the number and volume of water containers used by households for such purposes. This paper reports results from the application of machine learning (ML) techniques to a database of images of domestic water storage collected during 2022 as part of an initiative to improve water supply in southern Bangladesh. Because the number of different water container types was relatively small, it was possible to train an ML algorithm to identify water containers and estimate water storage with greater than 90% accuracy. These results have allowed the rapid creation of a unique high-quality, high-resolution dataset describing water storage quantitatively in a study community. This dataset includes data quantifying the number of vessels as well as their individual and aggregated water storage volumes. The paper discusses policy implications for the study location specifically before concluding with suggestions for the inclusion of this sort of analysis in ongoing studies of household and community scale water insecurity.
Authors: Chad Staddon, Ajmal Shahbaz, Syed U. Yunas, Lyndon Smith, Geraint Burrows, Sayed Mohammed Nazim Uddin, Lucy Whitley
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410127563