Sophisticated computer modeling has led to great progress in predicting weather-related disasters and their potential human tolls and economic effects. The predictable power of the models has given insurers comfort prescription coverage for risks – such as floods – that were once considered immobile and enabled them to develop innovative products.
It can be tempting to think of hurricane forecasts and modeling, which are all about high-resolution images, big data and elaborate algorithms. While these technologies are crucial to developing and implementing effective models, they rely heavily on local knowledge and "boots on the ground."
"After an incident, we quickly send engineers to map structural damage and look for links to storm characteristics," said Jeff Waters, senior product manager for RMS risk models. "Information collected by our people on the ground is integrated into our reconstruction of the incident to help us identify drivers of the damage and inform our models."
Waters reported that, in the wake of Hurricane Maria 201
"These buildings worked very well," Waters said. “Reinforced concrete prevents significant structural damage, and with less plaster and tile floors, internal damage due to water penetration is limited. Wood and light metal structures – which are usually in older districts where fewer properties are insured – performed much worse.
Such ground level information not only helped to validate the RMS loss calculation – it also contributes to the continuous improvement of the model. You can read a more detailed account on the RMS blog.
Recent research illustrates how advances in geospatial technology make it possible to integrate qualitative local knowledge into mathematical models to evaluate potential results of restoration and protection projects and to support plans to mitigate and recover. Local knowledge mapping is one such approach to marrying modern technology and the advanced analysis that facilitates the experiences of individuals, communities and companies that have been most affected by natural disasters.