Leeds Beckett student creates AI tool that predicts damp and mould in homes

Gulala Aziz’s tool has been trained using over 2,000 home inspection reports in England

2 days ago | UPDATED 19 hours ago

A Leeds Beckett student has created an artificial intelligence (AI) tool that can predict damp and mould in homes.

Gulala Aziz, a PhD student at the university’s sustainability institute, has developed the tool to support housing providers in managing damp and mould in properties.

Gulala’s tool is designed to predict which homes will be more likely to develop mould, meaning homes which may need more frequent inspections can be flagged to prevent risk to tenants, Housing Digital reports.

Created using “machine learning with explainable AI”, the tool has been trained using over 2,000 home inspection reports across 125 local authorities in England.

After the death of two-year-old Awaab Ishak from a respiratory condition caused by prolonged exposure to damp and mould in his social housing flat, Awaab’s law was introduced, bringing the impact of poor air quality in homes under increasing scrutiny.

The law forces social landlords to investigate and fix dangerous damp and mould in set time periods, and repair all emergency hazards within 24 hours.

Awaab’s death was one of 204,000 annual deaths linked to poor air quality in homes, with increasing levels of risk seen amongst vulnerable groups.

Gulala’s tool assesses a property’s features to evaluate its risk, including wall insulation, heating costs and energy efficiency.

After three years in development, the AI model’s checks have been finalised. This work represents the first peer-reviewed application of AI on damp and mould risk, and is featured in a Nature Scientific report.

Speaking about the research that went into development, Gulala said: “Housing associations and local authorities are subject to increasing requirements and scrutiny – and this research can help then to take swifter, more effective action.”