Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
Y Li, M Li, C Li, Z Liu - Scientific reports, 2020 - nature.com
Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle
and climate change in the global terrestrial ecosystem. AGB estimation based on remote …
and climate change in the global terrestrial ecosystem. AGB estimation based on remote …
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method
Flash-floods are increasingly recognized as a frequent natural hazard worldwide. Iran has
been among the most devastated regions affected by the major floods. While the temporal …
been among the most devastated regions affected by the major floods. While the temporal …
Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
H Tyralis, G Papacharalampous… - Neural Computing and …, 2021 - Springer
Daily streamflow forecasting through data-driven approaches is traditionally performed
using a single machine learning algorithm. Existing applications are mostly restricted to …
using a single machine learning algorithm. Existing applications are mostly restricted to …
CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain
We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-
GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates …
GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates …
[HTML][HTML] Advancing hydrology through machine learning: insights, challenges, and future directions using the CAMELS, caravan, GRDC, CHIRPS, PERSIANN, NLDAS …
Machine learning (ML) applications in hydrology are revolutionizing our understanding and
prediction of hydrological processes, driven by advancements in artificial intelligence and …
prediction of hydrological processes, driven by advancements in artificial intelligence and …
Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms
Y Li, C Li, M Li, Z Liu - Forests, 2019 - mdpi.com
Forest biomass is a major store of carbon and plays a crucial role in the regional and global
carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping …
carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping …
A random forest model for inflow prediction at wastewater treatment plants
Influent flow of wastewater treatment plants (WWTPs) is a crucial variable for plant operation
and management. In this study, a random forest (RF) model was applied for daily …
and management. In this study, a random forest (RF) model was applied for daily …
A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term …
Streamflow forecasting is an important component of water resources planning and
management. Data-Driven Models (DDMs) are common approaches for streamflow …
management. Data-Driven Models (DDMs) are common approaches for streamflow …
Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS
Post-processing of hydrological model simulations using machine learning algorithms can
be applied to quantify the uncertainty of hydrological predictions. Combining multiple …
be applied to quantify the uncertainty of hydrological predictions. Combining multiple …
Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins
The distribution of flowmeter data and basin characteristic information exhibits substantial
disparities, with most flow observations being recorded at a limited number of well …
disparities, with most flow observations being recorded at a limited number of well …