ART-RISK 3.0 a fuzzy—based platform that combine GIS and expert assessments for conservation strategies in cultural heritage

Natural hazards affecting cultural heritage: assessment of flood and landslide risk for the 28 existing Norwegian stave churches

“…Although CRS program still sees resistance between central and local governments, the researchers have found that "non-structural" methods, such as those measured by the CRS rating, were more than twice as effective as "structural" measures in DRR. Looking at the number of churches investigated in each study, interestingly, for flooding risk assessment often from 7 to 30 churches locations are compared (Ortiz et al, 2016;Moreno et al, 2022;Loreto et al, 2021). For landslide, only Pascale et al in 2013 assess a risk prone area with more than one church, while all the other reviewed studies remain focused on a single church.…”

“…Similarly, this has been done for landslides, although for this hazard is more common to have building structural monitoring performed in continuous or sporadic geotechnical or laser scanner surveys at support of a risk assessment over an extended time (Cambiaggi and Berardi, 2019;Indirli et al, 2011;Cigna et al, 2013). The literature review shows clearly that the research community is well aligned in terms of research-driven policy framework, as all the works related to churches affected by flooding quote the EU FD, several the Sendai DRR framework, while few the INSPIRE directive IJBPA 42,1 (Moreno et al, 2022;Cuca, 2020). Recent EU and National projects who have driven research on Flood risk assessment and management are: the EU-FP6 Cultural Heritage Protection against Flooding (CHEF), the Risk and Vulnerability in Cultural Heritage (RIVUPH) project funded by Andalusian Government (Ortiz et al, 2016), the EU Risk Indicators for the Analysis of Cultural heritage under Threat project (RIACT) (Figueiredo et al, 2021), the Italian MICHEmitigating the impact of natural hazards on cultural heritage sites, structures and artefacts project for the city of Florence, the NOAH web application program with historical/ real time natural hazards and weather events in the Philippines (Loreto et al, 2021) and the National Flood Insurance program for the Federal Emergency Management Agency (FEMA) high-risk ranking zone in USA (Paille et al, 2016).…”

“…This helps experts in documenting losses of valuable material or in gaining better insights on the induced damages. More recently, as reported by Romao and Bertolin in 2022, these topics started to involve the combination of machine learning (ML) techniques with remote-sensed data for identifying the threat level of multi-hazards for early warning systems development (Moreno et al, 2022) or for evaluating and mapping landslide susceptibility. The latter was done by Pascale et al (2013) using an artificial neural network (ANN) that requires the definition of appropriate thematic layers to parameterize the area under study.…”

Remote sensing to assess the risk for cultural heritage: forecasting potential collapses due to rainfall in historic fortifications

“…The Art-Risk 3.0 software (Moreno et al, 2022a;Cagigas-Muñiz et al, 2020) is designed to analyse groups of heritage buildings and assess which ones are at higher risk of loss. Applied to the monitoring of rainfall hazards, the Art-Risk 3.0 tool has a GIS (Moreno et al, 2022b) that, by entering the coordinates of a heritage asset located in Spain, indicates the hazard values according to average precipitation, it's intensity and the probability of flooding from the climate data from the last 30 years (Ortiz Calderon et al, 2021). The Art-Risk 1 model is based on a Leopold matrix adapted to record vulnerability according to conservation status.…”

“…For this, the use of the different hierarchical modules of the Art-Risk methodology is proposed. The Art-Risk 3.0 module (Moreno et al, 2022b) quickly categorises the climatic hazard due to rainfall into the three contexts analysed. The second, more complex module describes the risk scenario through the use of Art-Risk 1 vulnerability matrices (Moreno et al, 2019;Ortiz Calderon et al, 2021) and satellite images from the Global Precipitation Measurement (GPM) and Global Satellite Mapping of Precipitation (GSMaP) missions.…”