Preservation storage in a flood damage mitigation effort at the National Library of France

IFLA Journal ◽  
2021 ◽  
pp. 034003522110377
Author(s):  
Céline Allain ◽  
Sophie Guérinot

During a flood alert, the decision to evacuate a threatened collection of a library is an important one. If not thought out carefully, a hastily executed move can expose valuable collections to unforeseen threats. Although floods are usually slow to develop in Paris, the decision to make a preventive evacuation must be taken at the appropriate moment, considering the time needed for the relocation, the reality of the threat and the need for service continuity. In the context of its flood protection plan, the National Library of France has conceived a box model that contributes to saving time in case of a flood and prevents damage during an evacuation. Combining accessibility to documents with security requirements, this model can be implemented in different contexts.

Author(s):  
Juichiro AKIYAMA ◽  
Mirei SHIGE-EDA ◽  
Kouhei OHBA ◽  
Masato YAMAO ◽  
Hiroaki IWAMOTO

2014 ◽  
Vol 40 ◽  
pp. 69-77 ◽  
Author(s):  
Jennifer K. Poussin ◽  
W.J. Wouter Botzen ◽  
Jeroen C.J.H. Aerts

2021 ◽  
Vol 21 (6) ◽  
pp. 257-264
Author(s):  
Hoseon Kang ◽  
Jaewoong Cho ◽  
Hanseung Lee ◽  
Jeonggeun Hwang ◽  
Hyejin Moon

Urban flooding occurs during heavy rains of short duration, so quick and accurate warnings of the danger of inundation are required. Previous research proposed methods to estimate statistics-based urban flood alert criteria based on flood damage records and rainfall data, and developed a Neuro-Fuzzy model for predicting appropriate flood alert criteria. A variety of artificial intelligence algorithms have been applied to the prediction of the urban flood alert criteria, and their usage and predictive precision have been enhanced with the recent development of artificial intelligence. Therefore, this study predicted flood alert criteria and analyzed the effect of applying the technique to augmentation training data using the Artificial Neural Network (ANN) algorithm. The predictive performance of the ANN model was RMSE 3.39-9.80 mm, and the model performance with the extension of training data was RMSE 1.08-6.88 mm, indicating that performance was improved by 29.8-82.6%.


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