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Identifying Nickel Contamination in Soil Using Vis-Nir Data and Machine Learning: Dealing with Imbalanced Datasets

https://doi.org/10.2139/ssrn.4776210
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inferredcites →https://doi.org/10.1002/saj2.20123openalex-extracted
Comparing publicly available databases to evaluate soil organic carbon in Maine, USA
inferredcites →https://doi.org/10.1007/s11356-023-25806-yopenalex-extracted
Phytoextraction of nickel, lead, and chromium from contaminated soil using sunflower, marigold, and spinach: comparison of efficiency and fractionation study
inferredcites →https://doi.org/10.1016/j.apgeochem.2005.01.009openalex-extracted
Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils
inferredcites →https://doi.org/10.1016/j.chemosphere.2008.09.003openalex-extracted
Comparison of monometal and multimetal adsorption in Mississippi River alluvial wetland sediment: Batch and column experiments
inferredcites →https://doi.org/10.1016/j.coldregions.2023.103928openalex-extracted
Prediction of the jump height of transmission lines after ice-shedding based on XGBoost and Bayesian optimization
inferredcites →https://doi.org/10.1016/j.ecolind.2022.109330openalex-extracted
Estimating the heavy metal contents in farmland soil from hyperspectral images based on Stacked AdaBoost ensemble learning
inferredcites →https://doi.org/10.1016/j.envint.2019.02.011openalex-extracted
Health risk assessment of potentially toxic elements in soils along the Central Elbe River, Germany
inferredcites →https://doi.org/10.1016/j.envres.2021.111799openalex-extracted
A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China
inferredcites →https://doi.org/10.1016/j.envres.2022.114286openalex-extracted
XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions
inferredcites →https://doi.org/10.1016/j.geoderma.2015.07.006openalex-extracted
Mapping topsoil physical properties at European scale using the LUCAS database
inferredcites →https://doi.org/10.1016/j.geoderma.2022.116301openalex-extracted
Improving soil organic matter estimation accuracy by combining optimal spectral preprocessing and feature selection methods based on pXRF and vis-NIR data fusion
inferredcites →https://doi.org/10.1016/j.gr.2022.10.004openalex-extracted
Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms
inferredcites →https://doi.org/10.1016/j.isprsjprs.2017.12.003openalex-extracted
Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges
inferredcites →https://doi.org/10.1016/j.jag.2023.103583openalex-extracted
Spectral unmixing-based Arctic plant species analysis using a spectral library and terrestrial hyperspectral Imagery: A case study in Adventdalen, Svalbard
inferredcites →https://doi.org/10.1016/j.jclepro.2023.138673openalex-extracted
Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate
inferredcites →https://doi.org/10.1016/j.jenvman.2012.12.032openalex-extracted
Analysis of worldwide regulatory guidance values for the most commonly regulated elemental surface soil contamination
inferredcites →https://doi.org/10.1016/j.jenvman.2019.109778openalex-extracted
Release of toxic elements in fishpond sediments under dynamic redox conditions: Assessing the potential environmental risk for a safe management of fisheries systems and degraded waterlogged sediments
inferredcites →https://doi.org/10.1016/j.jenvman.2023.118817openalex-extracted
Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method
inferredcites →https://doi.org/10.1016/j.jhazmat.2019.06.004openalex-extracted
The application of machine learning methods for prediction of metal sorption onto biochars
inferredcites →https://doi.org/10.1016/j.jhazmat.2023.130722openalex-extracted
Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery
inferredcites →https://doi.org/10.1016/j.petrol.2021.109520openalex-extracted
An optimized XGBoost method for predicting reservoir porosity using petrophysical logs
inferredcites →https://doi.org/10.1016/s0034-4257(96)00120-4openalex-extracted
The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process
inferredcites →https://doi.org/10.1021/ac60214a047openalex-extracted
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
inferredcites →https://doi.org/10.1021/acs.jcim.3c00670openalex-extracted
Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization
inferredcites →https://doi.org/10.1021/acssuschemeng.1c08522openalex-extracted
Guiding the Optimization of Membrane Electrode Assembly in a Proton Exchange Membrane Water Electrolyzer by Machine Learning Modeling and Black-Box Interpretation
inferredcites →https://doi.org/10.1021/acssuschemeng.2c05104openalex-extracted
Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste
inferredcites →https://doi.org/10.1023/a:1012406528296openalex-extracted
Support Vector Machines for Classification in Nonstandard Situations
inferredcites →https://doi.org/10.1080/15226514.2021.1929826openalex-extracted
Calcite in combination with olive pulp biochar reduces Ni mobility in soil and its distribution in chili plant
inferredcites →https://doi.org/10.1093/jamia/ocz036openalex-extracted
Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort
inferredcites →https://doi.org/10.1111/ejss.12499openalex-extracted
LUCAS Soil, the largest expandable soil dataset for Europe: a review
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-extracted
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.2136/sssaj2016.02.0052openalex-extracted
Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project
inferredcites →https://doi.org/10.3389/fimmu.2019.00674openalex-extracted
Parameter Identification for a Model of Neonatal Fc Receptor-Mediated Recycling of Endogenous Immunoglobulin G in Humans
inferredcites →https://doi.org/10.3389/fncel.2019.00150openalex-extracted
Urine Organic Acids as Potential Biomarkers for Autism-Spectrum Disorder in Chinese Children
inferredcites →https://doi.org/10.3390/bioengineering9080350openalex-extracted
A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease
inferredcites →https://doi.org/10.3390/ijerph17197349openalex-extracted
Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
inferredcites →https://doi.org/10.3390/metabo3020277openalex-extracted
Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches
inferredcites →https://doi.org/10.3390/nu14081626openalex-extracted
Dietary Intake of Toxic Heavy Metals with Major Groups of Food Products—Results of Analytical Determinations
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