[HTML][HTML] Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

X Zhou, H Wen, Y Zhang, J Xu, W Zhang - Geoscience Frontiers, 2021 - Elsevier
The present study aims to develop two hybrid models to optimize the factors and enhance
the predictive ability of the landslide susceptibility models. For this, a landslide inventory …

[HTML][HTML] Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

PTT Ngo, M Panahi, K Khosravi, O Ghorbanzadeh… - Geoscience …, 2021 - Elsevier
The identification of landslide-prone areas is an essential step in landslide hazard
assessment and mitigation of landslide-related losses. In this study, we applied two novel …

A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment

M Ghobaei-Arani, A Shahidinejad - Expert Systems with Applications, 2022 - Elsevier
The rapid development of Internet of Things (IoT)-based applications and the era of 5G
networks has led to an exponential increase in the amount of data required for processing …

[HTML][HTML] A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide …

J Ma, D Xia, Y Wang, X Niu, S Jiang, Z Liu… - … Applications of Artificial …, 2022 - Elsevier
Abstract Machine learning (ML) has been extensively applied to model geohazards, yielding
tremendous success. However, researchers and practitioners still face challenges in …

Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea

WL Hakim, F Rezaie, AS Nur, M Panahi… - Journal of environmental …, 2022 - Elsevier
Landslides are a geological hazard that can pose a serious threat to human health and the
environment of highlands or mountain slopes. Landslide susceptibility mapping is an …

GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods

X Chen, W Chen - Catena, 2021 - Elsevier
Globally, but especially in China, landslides are considered to be one of the most severe
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

FE Jalal, M Iqbal, WA Khan, A Jamal, K Onyelowe… - Scientific Reports, 2024 - nature.com
This research suggests a robust integration of artificial neural networks (ANN) for predicting
swell pressure and the unconfined compression strength of expansive soils (P s UCS-ES) …

A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

D Van Dao, A Jaafari, M Bayat, D Mafi-Gholami, C Qi… - Catena, 2020 - Elsevier
With the increasing threat of recurring landslides, susceptibility maps are expected to play a
bigger role in promoting our understanding of future landslides and their magnitude. This …

A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping

Z Fang, Y Wang, L Peng, H Hong - International Journal of …, 2021 - Taylor & Francis
This study introduces four heterogeneous ensemble-learning techniques, that is, stacking,
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …

Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based …

H Shahabi, A Shirzadi, K Ghaderi, E Omidvar… - Remote Sensing, 2020 - mdpi.com
Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we
propose a new flood susceptibility mapping technique. We employ new ensemble models …