A completer picture of domestic water access and consumption

A completer picture of domestic water access and consumption: Integrating machine learning models and survey information

Geleijnse, Jan (TU Delft Civil Engineering and Geosciences)

Abraham, E. (mentor)
Rutten, M.M. (graduation committee)
de Villiers, D.J. (graduation committee)
van Halem, D. (graduation committee)

Delft University of Technology

Civil Engineering

African Water Corridor (AWC) project

2022

The United Nations (UN) Sustainable Development Goal (SDG) \#6 reads that by 2030 universal and equitable access to safe and affordable drinking water is achieved for all In order to achieve this goal, proper and complete monitoring, capturing all the facets of safe water access, is essential. In this thesis it is argued that the current monitoring techniques come with limitations and subsequently solutions are presented to come to a more complete picture of water access.

Monitoring safe water access happens primarily through household health surveys. These surveys are often incomplete, not covering entire nations, focus on only the primary water source and are often spatially aggregated for privacy reasons. Besides, health surveys almost never include questions on consumed water volumes while that is an important indicator for proper hygiene (WELL, 1998), and something that, at the same time, should be in balance with the natural available water resources. Next to this survey based monitoring, there is the Water Point Data Exchange (WPDx) that monitors safe access by providing a platform at which the exact location and type of water access points (such as boreholes, springs, etc.) are registered. This does give more insight into the presence and usage of a variety of sources, but also the WPDx is often incomplete: not covering entire nations.

In this thesis we present a dual methodology that gap-fills the incompleteness of the WPDx database through modeling and in parallel, researches the complex local dynamics of water access, the variety of water sources used by households and the relationships between access and water consumption by means of a household survey.

By improving a machine learning biological species modeling technique (called MaxEnt), successful predictions on the number of presences of eight different water access types across Uganda were made, also into areas that have little presence in the WPDx data. It was found that population density, precipitation, elevation, poverty and groundwater storage are important indicators for the (non)presence of water access points.

Next to modeling, a survey campaign was executed in Bushenyi-Ishaka municipality, a mid-sized town in the South West of Uganda comprising a mixture of both urban and rural areas. This was done in collaboration with Makerere University (Kampala). The survey results showed that water consumption increases with education and wealth, but also with higher number of water point presences predicted by the model. It was also found that households in Bushenyi make use of an average of two different water sources on a regular basis and often express preference for sources off premises compared to on premises (piped) for both cost and perceived quality reasons.

Lastly, modi operandi were suggested for the results to improve water access such as prioritising areas with poor(est) water access and investing in rainwater harvesting, infrastructure and education.

Water
water access
water consumption
Uganda
Bushenyi
African Water Corridor
Machine learning
presence only
survey
MaxEnt
Neural Networks
Modeling
Makerere University

http://resolver.tudelft.nl/uuid:9fbd593a-3748-4cd5-a60f-db1b204901ef

2022-09-03

-0.5408687736206675, 30.159213434825606

Student theses

master thesis

© 2022 Jan Geleijnse