Life loss estimation of dam and levee failures

2011 ◽  
pp. 147-152
Author(s):  
W Lehman ◽  
J Needham
Keyword(s):  
Loss Estimation

Correction: Cryogenic Characterization and Modeling of Silicon Superjunction MOSFET for Power Loss Estimation

2020 ◽  
Author(s):  
Md Maksudul Hossain ◽  
Arman Ur Rashid ◽  
Rosten Sweeting ◽  
Yuqi Wei ◽  
Haider Mhiesan ◽  
...  
Keyword(s):  
Power Loss ◽  
Loss Estimation

Transmission loss estimation for ephemeral sand rivers in Southern Africa

2021 ◽  
pp. 126487
Author(s):  
Simon A. Mathias ◽  
Sim M. Reaney ◽  
Piet K. Kenabatho
Keyword(s):  
Southern Africa ◽  
Transmission Loss ◽  
Loss Estimation

scholarly journals Accelerate disaster risk mapping using an automated zonal change detection model for loss estimation and recovery: a flood case study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ali Amasha
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Flash Flood ◽  
Disaster Risk ◽  
Loss Estimation ◽  
Risk Mapping ◽  
Detection Model ◽  
Applied Model ◽  
High Resolution Satellite Images ◽  
Before And After

Abstract Background The flash flood still constitutes one of the major natural meteorological disasters harmfully threatening local communities, that creates life losses and destroying infrastructures. The severity and magnitude of disasters always reflected from the size of impacts. Most of the conventional research models related to flooding vulnerability are focusing on hydro-meteorological and morphometric measurements. It, however, requires quick estimate of the flood losses and assess the severity using reliable information. An automated zonal change detection model applied, using two high-resolution satellite images dated 2009 and 2011 coupled with LU/LC GIS layer, on western El-Arish City, downstream of Wadi El-Arish basin. The model enabled to estimate the severity of a past flood incident in 2010. Results The model calculated the total changes based on the before and after satellite images based on pixel-by-pixel comparison. The estimated direct-damages nearly 32,951 m2 of the total mapped LU/LC classes; (e.g., 11,407 m2 as 3.17% of the cultivated lands; 6031 m2 as 7.22% of the built-up areas and 4040 m2 as 3.62% of the paved roads network). The estimated cost of losses, in 2010 economic prices for the selected three LU/LC classes, is nearly 25 million USD, for the cultivation fruits and olives trees, ~ 4 million USD for built-up areas and ~ 1 million USD for paved roads network. Conclusion The disasters’ damage and loss estimation process takes many detailed data, longtime, and costed as well. The applied model accelerates the disaster risk mapping that provides an informative support for loss estimation. Therefore, decision-makers and professionals need to apply this model for quick the disaster risks management and recovery.

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