Accueil
Atlas
Analyses
Documentation
Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Type de record:

paper

Créateur:

Moh

Éditeur:

Elsevier BV

Hôte:

Visit

Languages

Arabic, Algerian Spoken

Licenses

https://www.elsevier.com/tdm/userlicense/1.0/

Similaires

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning ModelsMachine Learning-Based Landslide Susceptibility Mapping and Environmental Controls in Hanang District, Northern TanzaniaLandslide Susceptibility Mapping using Statistical Methods in Uatzau Catchment Area, Northwestern EthiopiaLandslide mapping using satellite imagery and machine learning algorithmsJoseph-Munyenze/Murang-a-Landslide-Susceptibility-MappingPredicting Landslide Susceptibility in Cross River State of Nigeria using Machine Learning

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning Models

Landslide situations remain a significant concern in Nigeria, as they pose a great risk to human liv

Machine Learning-Based Landslide Susceptibility Mapping and Environmental Controls in Hanang District, Northern Tanzania

Abstract Landslides are among the most destructive geohazards in mountainous regio

Landslide Susceptibility Mapping using Statistical Methods in Uatzau Catchment Area, Northwestern Ethiopia

Abstract Landslide susceptibility mapping is important to hazard management and to have pl

Landslide mapping using satellite imagery and machine learning algorithms

Cyclone Idai made landfall on 15th March near Beira, Mozambique, and caused heavy rainfall across Mo

Joseph-Munyenze/Murang-a-Landslide-Susceptibility-Mapping

This repository contains code, data, and models for landslide susceptibility mapping in Murang'a Cou

Predicting Landslide Susceptibility in Cross River State of Nigeria using Machine Learning

<p>Landslides have continued to wreck its havoc in many parts of the globe; comprehens