Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial Neural Networks—An Example of a Heavy Rainfall Event in Henan Province

Cultural relic conservation capability is an important issue in cultural relic conservation research,and it is critical to decrease the vulnerability of immovable cultural relics to rainfall hazards.Commonly used vulnerability assessment methods are subjective,are mostly applied to regional conditions,and cannot accurately assess the vulnerability of cultural relics.In addition,it is impossible to predict the future vulnerability of cultural relics.Therefore,this study proposed a machine learning-based vulnerability assessment method that not only can assess cultural relics individually but also predict the vulnerability of cultural relics under different rainfall hazard intensities.An extreme rainfall event in Henan Province in 2021 was selected as an example,with a survey report on the damage to cultural relics as a database.The results imply that the back propagation(BP)neural network-based method of assessing the vulnerability of immovable cultural relics is reliable,with an accuracy rate higher than 92%.Based on this model to predict the vulnerability of Zhengzhou City’s cultural relics,the vulnerability levels of cultural relics under different recurrence periods of heavy rainfall were obtained.Among them,the vulnerability of ancient sites is higher than those of other cultural relic types.The assessment model used in this study is suitable for predicting the vulnerability of immovable cultural relics to heavy rainfall hazards and can provide a technical means for cultural relic conservation studies.

Natural disaster in the mountainous region of Rio de Janeiro state,Brazil:Assessment of the daily rainfall erosivity as an early warning index

Rainfall erosivity is defined as the potential of rain to cause erosion.It has great potential for application in studies related to natural disasters,in addition to water erosion.The objectives of this study were:ⅰ)to model the Rday using a seasonal model for the Mountainous Region of the State of Rio de Janeiro(MRRJ);ⅱ)to adjust thresholds of the Rday index based on catastrophic events which occurred in the last two decades;andⅲ)to map the maximum daily rainfall erosivity(Rmaxday)to assess the region's suscepti-bility to rainfall hazards according to the established Rday limits.The fitted Rday model presented a satisfactory result,thereby enabling its application as a Rday estimate in MRRJ.Events that resulted in Rday>1500 MJ ha-1.mm.h-1.day-1 were those with the highest number of fatalities.The spatial distribution of Rmaxday showed that the entire MRRJ has presented values that can cause major rainfall.The Rday index proved to be a promising indicator of rainfall disasters,which is more effective than those normally used that are only based on quantity(mm)and/or intensity(mm.h-1)of the rain.