Combining Artificial Intelligence, remote sensing and computer modelling for the design of Nature Based Solutions

Currently, a substantial proportion of power stations, railway infrastructure, wastewater treatment facilities, and residential areas are at risk of coastal flooding, resulting in significant annual economic losses. Hard engineering solutions are becoming economically unviable due to the high costs of construction, maintenance, and adaptation to changes in sea level and storms.

For this reason, there is a growing interest in engineering with nature (including the creation of salt marshes, seagrass beds, beach nourishment, and mega-nourishment), which offers a more economically viable alternative and supports net zero-carbon emissions and local amenity value, as highlighted in the 25-year Government Plan to Improve the Environment and the FCERM strategies for England, Scotland, and Wales.

However, despite the growing recognition of the necessity to move towards this greener alternative for coastal protection, there is still limited guidance on the implementation of engineering with nature compared to hard engineering solutions. There are no quantitative and process-based decision-making tools or guidelines to aid engineers, planners, and governments in selecting coastal management strategies suited to their unique local environments. There remain many uncertainties regarding the conditions that maximize the establishment and longevity of engineering with nature, as well as uncertainties regarding its effectiveness.

The project ENARM develops novel understanding necessary to protect coastal infrastructure and coastal communities through the widespread adoption of engineering with nature. ENARM uses a novel combination of remote sensing, artificial intelligence, and computer models to provide, for the first time, design criteria for coastal protection using engineering with nature, as well as the knowledge necessary to select the most durable and efficient coastal management type and location.

Results are summarised into interactive decision-support tools, to enable a consistent evaluation of the pros and cons of different coastal management interventions, including uncertainties related to their effectiveness under different sea-level rise and storm scenarios.