Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review
This article reviews the current research and applications of various digital soil mapping
(DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks …
(DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks …
[HTML][HTML] Space-time landslide predictive modelling
Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties,
and the environment in many areas. Investigators have for long attempted to estimate …
and the environment in many areas. Investigators have for long attempted to estimate …
High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning …
Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil
health and play a key role in the global carbon and nitrogen cycles. High-resolution radar …
health and play a key role in the global carbon and nitrogen cycles. High-resolution radar …
[HTML][HTML] Presenting logistic regression-based landslide susceptibility results
L Lombardo, PM Mai - Engineering geology, 2018 - Elsevier
A new work-flow is proposed to unify the way the community shares Logistic Regression
results for landslide susceptibility purposes. Although Logistic Regression models and …
results for landslide susceptibility purposes. Although Logistic Regression models and …
[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 …
Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning
B Kasraei, B Heung, DD Saurette, MG Schmidt… - … Modelling & Software, 2021 - Elsevier
Digital soil mapping (DSM) techniques have provided soil information that has
revolutionized soil management across multiple spatial extents and scales. DSM …
revolutionized soil management across multiple spatial extents and scales. DSM …
[HTML][HTML] Spatial modeling of multi-hazard threat to cultural heritage sites
Cultural heritage is the foundation upon which global and historical values are based on. It
connects us to the legacy left by our ancestors and identifies who we are as part of the …
connects us to the legacy left by our ancestors and identifies who we are as part of the …
[HTML][HTML] Landslide size matters: A new data-driven, spatial prototype
The standard definition of landslide hazard requires the estimation of where, when (or how
frequently) and how large a given landslide event may be. The geoscientific community …
frequently) and how large a given landslide event may be. The geoscientific community …
Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation
F Veronesi, C Schillaci - Ecological Indicators, 2019 - Elsevier
In recent years, the environmental modeling community has moved away from kriging as the
main mapping algorithm and embraced machine learning (ML) as the go-to method for …
main mapping algorithm and embraced machine learning (ML) as the go-to method for …