A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities

W Han, X Zhang, Y Wang, L Wang, X Huang… - ISPRS Journal of …, 2023 - Elsevier
Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …

[HTML][HTML] Multi-hazard susceptibility map** based on Convolutional Neural Networks

K Ullah, Y Wang, Z Fang, L Wang, M Rahman - Geoscience Frontiers, 2022 - Elsevier
Multi-hazard susceptibility prediction is an important component of disasters risk
management plan. An effective multi-hazard risk mitigation strategy includes assessing …

[HTML][HTML] DEM resolution effects on machine learning performance for flood probability map**

M Avand, A Kuriqi, M Khazaei… - Journal of Hydro …, 2022 - Elsevier
Floods are among the devastating natural disasters that occurred very frequently in arid
regions during the last decades. Accurate assessment of the flood susceptibility map** is …

Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination

HR Pourghasemi, S Pouyan, M Bordbar, F Golkar… - Natural Hazards, 2023 - Springer
Protection against natural hazards (ie, floods, landslides, forest fires, and earthquakes) is
vital in land-use planning, especially in high-risk areas. Multi-hazard susceptibility maps can …

Landslide and wildfire susceptibility assessment in Southeast Asia using ensemble machine learning methods

Q He, Z Jiang, M Wang, K Liu - Remote Sensing, 2021 - mdpi.com
Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on
susceptibility modeling for the two hazards together have been conducted for this region …

Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards

Ö Ekmekcioğlu, K Koc - Catena, 2022 - Elsevier
This research proposes a novel step-wise binary prediction framework for the susceptibility
assessment of geo-hydrological hazards specific to floods and landslides. The framework of …

Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability

T Zhang, D Wang, Y Lu - Scientific reports, 2023 - nature.com
The regional multi-hazards risk assessment poses difficulties due to data access challenges,
and the potential interactions between multi-hazards and social vulnerability. For better …

GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling

W Chen, S Zhang - Catena, 2021 - Elsevier
Landslides, one of the most common hazards around the world, have brought about severe
damage to life and property of human. To prevent and mitigate landslides, various models …

Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea

Y Piao, D Lee, S Park, HG Kim, Y ** - Geomatics, Natural Hazards …, 2022 - Taylor & Francis
Forest fires are one of the most frequently occurring natural hazards, causing substantial
economic loss and destruction of forest cover. As the Gangwon-do region in Korea has …

Wildfire hazard map** in the eastern Mediterranean landscape

A Trucchia, G Meschi, P Fiorucci… - … journal of wildland …, 2023 - CSIRO Publishing
Background Wildfires are a growing threat to many ecosystems, bringing devastation to
human safety and health, infrastructure, the environment and wildlife. Aims A thorough …