Machine learning and landslide studies: recent advances and applications
Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …
today, to the landslide community, many studies have been carried out to explore the …
[HTML][HTML] Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future
Landslides are geomorphological processes that shape the landscapes of all continents,
dismantling mountains and contributing sediments to the river networks. Caused by …
dismantling mountains and contributing sediments to the river networks. Caused by …
Deep learning forecast of rainfall-induced shallow landslides
Rainfall triggered landslides occur in all mountain ranges posing threats to people and the
environment. Given the projected climate changes, the risk posed by landslides is expected …
environment. Given the projected climate changes, the risk posed by landslides is expected …
[HTML][HTML] An updating of landslide susceptibility prediction from the perspective of space and time
Due to the similarity of conditioning factors, the aggregation feature of landslides and the
multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be …
multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be …
Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments
The determination of mapping units, including grid, slope, unique condition, administrative
division, and watershed units, is a very important modeling basis for landslide assessments …
division, and watershed units, is a very important modeling basis for landslide assessments …
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 …
[HTML][HTML] Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling–Benefits of exploring landslide data collection effects
Data-driven landslide susceptibility models formally integrate spatial landslide information
with explanatory environmental variables that describe predisposing factors of slope …
with explanatory environmental variables that describe predisposing factors of slope …
[HTML][HTML] Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling
A Dahal, L Lombardo - Computers & geosciences, 2023 - Elsevier
For decades, the distinction between statistical models and machine learning ones has
been clear. The former are optimized to produce interpretable results, whereas the latter …
been clear. The former are optimized to produce interpretable results, whereas the latter …
[HTML][HTML] Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory
Landslide susceptibility corresponds to the probability of landslide occurrence across a
given geographic space. This probability is usually estimated by using a binary classifier …
given geographic space. This probability is usually estimated by using a binary classifier …
Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China
China has been subject to rapid urban expansion and afforestation since the economic
reform in 1978. However, the influence of land use and cover changes (LUCCs) and human …
reform in 1978. However, the influence of land use and cover changes (LUCCs) and human …