SEVUCAS: A novel GIS-based machine learning software for seismic vulnerability assessment
Since it is not possible to determine the exact time of a natural disaster's occurrence and the amount of physical and financial damage on humans or the environment resulting from their event, decision-makers need to identify areas with potential vulnerability in order to reduce future losses....
Main Authors: | Lee, Saro, Panahi, Mahdi, Pourghasemi, Hamid Reza, Shahabi, Himan, Alizadeh, Mohsen, Shirzadi, Ataollah, Khosravi, Khabat, Melesse, Assefa M., Yekrangnia, Mohamad, Rezaie, Fatemeh, Moeini, Hamidreza, Pham, Binh Thai, Ahmad, Baharin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019
|
Subjects: | |
Online Access: | http://eprints.utm.my/88635/1/BaharinAhmad2019_SEVUCASANovelGIS-BasedMachine.pdf |
Similar Items
-
A hybrid computational intelligence approach to groundwater spring potential mapping
by: Bui, Dieu Tien, et al.
Published: (2019) -
Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
by: Chen, Wei, et al.
Published: (2019) -
Application of GIS and remote sensing techniques in assessment of natural hazards in the Central Zab Basin, Northwest of Iran
by: Saeed, Khezri, et al.
Published: (2013) -
Application of GIS based multi-criteria analysis in site selection of water reservoirs (case study: Batu Pahat, Malaysia)
by: Ahmad, Bakhtyar Ali, et al.
Published: (2015) -
Land subsidence susceptibility mapping in South Korea using machine learning algorithms
by: Bui, D. T., et al.
Published: (2018)