[HTML][HTML] Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility
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 …
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 …
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 …
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
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 …
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
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 …
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
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 …
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 …
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
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 …
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
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 …
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
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 …
worldwide. We developed two novel hybrid predictive models that combine the self …