Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility

P Lima, S Steger, T Glade, FG Murillo-García - Journal of Mountain …, 2022 - Springer
In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on
statistical or machine learning approaches, have become popular to estimate the relative …

Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient

G Lin, A Lin, D Gu - Information Sciences, 2022 - Elsevier
The prediction of short-term traffic flow is critical for improving service levels for drivers and
passengers as well as enhancing the efficiency of traffic management in the urban …

[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 …

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 …

A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India

D Ruidas, R Chakrabortty, ARMT Islam, A Saha… - Environmental earth …, 2022 - Springer
The exponential growth in the number of flash flood events is a global threat, and detecting a
flood-prone area has also become a top priority. The flash flood-susceptibility mapping can …

[HTML][HTML] Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression

S Mehravar, SV Razavi-Termeh, A Moghimi… - Journal of …, 2023 - Elsevier
Flood has long been known as one of the most catastrophic natural hazards worldwide.
Mapping flood-prone areas is an important part of flood disaster management. In this study …

[HTML][HTML] Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

AL Balogun, F Rezaie, QB Pham, L Gigović… - Geoscience …, 2021 - Elsevier
In this study, we developed multiple hybrid machine-learning models to address parameter
optimization limitations and enhance the spatial prediction of landslide susceptibility models …

InSAR time-series analysis and susceptibility mapping for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression

WL Hakim, MF Fadhillah, S Park, B Pradhan… - Remote Sensing of …, 2023 - Elsevier
Global sea-level rise due to climate change is a critical problem for coastal cities. One of the
coastal cities in Indonesia, Semarang, is in danger of being submerged by seawater due to …

Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms

M Mehrabi, H Moayedi - Environmental Earth Sciences, 2021 - Springer
As a frequent natural disaster, landslides incur significant economic and human losses
worldwide. The main idea of this paper is to propose novel integrative models for landslide …

Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides

A Jaafari, M Panahi, D Mafi-Gholami, O Rahmati… - Applied Soft …, 2022 - Elsevier
The robustness of landslide prediction models has become a major focus of researchers
worldwide. We developed two novel hybrid predictive models that combine the self …