Updated soil salinity with fine spatial resolution and high accuracy: The synergy of Sentinel-2 MSI, environmental covariates and hybrid machine learning approaches
Soil salinization is the main source of global soil degradation. It has impeded progress
towards sustainable development goals (SDGs) by threatening 20% of irrigated areas …
towards sustainable development goals (SDGs) by threatening 20% of irrigated areas …
Comparative assessment of various machine learning‐based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in …
Forecasts of maximum and minimum air temperatures are essential to mitigate the damage
of extreme weather events such as heat waves and tropical nights. The Numerical Weather …
of extreme weather events such as heat waves and tropical nights. The Numerical Weather …
[HTML][HTML] Intelligent early-warning platform for open-pit mining: current status and prospects
Z Song, X Li, R Huo, L Liu - Rock Mechanics Bulletin, 2024 - Elsevier
As the profundity of open-pit mining operations has increased, so has the frequency of
geological disasters. The complex interaction of factors causing these disasters presents …
geological disasters. The complex interaction of factors causing these disasters presents …
Inadequate adaptation of geospatial information for sustainable mining towards agenda 2030 sustainable development goals
AW Moomen, M Bertolotto, P Lacroix… - Journal of Cleaner …, 2019 - Elsevier
For all the evolutionary ages of mineral resource development, there have not been
concerns about sustainable mining until the 21st century. Thus, this paper explores the …
concerns about sustainable mining until the 21st century. Thus, this paper explores the …
Nearest neighbour distance matching Leave‐One‐Out Cross‐Validation for map validation
Several spatial and non‐spatial Cross‐Validation (CV) methods have been used to perform
map validation when additional sampling for validation purposes is not possible, yet it is …
map validation when additional sampling for validation purposes is not possible, yet it is …
Quantifying western US rangelands as fractional components with multi-resolution remote sensing and in situ data
M Rigge, C Homer, L Cleeves, DK Meyer, B Bunde… - Remote Sensing, 2020 - mdpi.com
Quantifying western US rangelands as a series of fractional components with remote
sensing provides a new way to understand these changing ecosystems. Nine rangeland …
sensing provides a new way to understand these changing ecosystems. Nine rangeland …
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar
ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due …
ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due …
Novel machine learning-based energy consumption model of wastewater treatment plants
Wastewater treatment plants (WWTPs) can account for up to 1% of a country's energy
consumption. Meanwhile, WWTPs have high energy-saving potential. To achieve this, it is …
consumption. Meanwhile, WWTPs have high energy-saving potential. To achieve this, it is …
Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique
The reliable and robust monitoring of air temperature distribution is essential for urban
thermal environmental analysis. In this study, a stacking ensemble model consisting of multi …
thermal environmental analysis. In this study, a stacking ensemble model consisting of multi …
Machine learning approaches for detecting tropical cyclone formation using satellite data
This study compared detection skill for tropical cyclone (TC) formation using models based
on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF) …
on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF) …