Resilience Adaptation Approach for Reducing the Negative Impact of Climate Change on Coastal Heritage Sites through Machine Learning

Abstract

The continuous cumulative worsening impact of climate change on heritage sites represents a new challenge for most of the nonrenewable resources of heritage sites and buildings in general; this is especially true with respect to coastal heritage sites, which are facing a more dangerous situation as the climate becomes more extreme in coastal areas and sea levels rise, putting heritage sites at risk. A strict adaptation plan, usually made for reducing the impact of climate change, may not be the solution, as different heritage site locations, materials, and hazard types need tailored plans. Therefore, in this research paper, a resilience approach was introduced to help adapt the most problematic sites to the impacts of climate change, i.e., coastal heritage sites. To fulfill the objective of achieving adaptation in a resilient way that can easily be developed in relation to different types of sites, mixed research methods were used. First, the literature was reviewed using the Connected Papers tool. Then, machine learning methods were used to process and analyze the input data of the resilience adaptation plan for an Egyptian coastal heritage site case study, i.e., Alexandria. Next, the data were arranged and analyzed, highlighting the main classifying algorithms responsible for identifying the resilience range, using the machine learning software packages Infra Nodus and WEKA, according to the differences in the climate change impact at the heritage sites. The final outcome of this research is a resilience approach that can be adapted to rescue plans for coastal heritage sites via machine learning.

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