Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
Abstract The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors)...
Guardado en:
Autores principales: | Hamid Reza Pourghasemi, Narges Kariminejad, Mahdis Amiri, Mohsen Edalat, Mehrdad Zarafshar, Thomas Blaschke, Artemio Cerda |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e49a5dfb79724dd68433a0e3aa2570e7 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Evaluation of multi-hazard map produced using MaxEnt machine learning technique
por: Narges Javidan, et al.
Publicado: (2021) -
A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran
por: Soheila Pouyan, et al.
Publicado: (2021) -
Mapping wind erosion hazard with regression-based machine learning algorithms
por: Hamid Gholami, et al.
Publicado: (2020) -
Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
por: Dae-Hong Min, et al.
Publicado: (2021) -
Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models
por: Maryam Sadat Jaafarzadeh, et al.
Publicado: (2021)