Search results for learning models

Prediction of quaternary alkali metal hydroxide - water mixture melting points using machine learning

Roach, Lucien · Updated 19 Jul 2024

… In this work, we use an artificial neural network model to estimate the melting points of fluxes and …

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Australia-wide 30 m machine learning-derived canopy height models composites: best pick and median

CSIRO Data Access Portal · Updated 19 Nov 2025

… Machine Learning-Derived Canopy Height Models at Continental Scale.” The pick-CHM is a composite model in which …

Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium

Geoscience Australia Data · Updated 16 Oct 2025

… The high AUC of the deep learning model demonstrates that public geospatial data can accurately predict natural resources …

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Bass Strait Sediment Grain Size Prediction Grids

Australian Ocean Data Network · Updated 12 Aug 2026

… These grids were the results of the predictive modelling using machine learning models. The inputs to the machine …

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Dioptra Test Platform

National Institute of Standards and Technology · Updated 24 Jul 2024

… Use Cases We envision the following primary use cases for Dioptra: - Model Testing: -- 1st party - Assess AI models …

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Forest Structure - Sentinel-1/2, GEDI - Germany, Yearly

open.bydata · Updated 11 Nov 2026

… The products were generated using a machine learning modelling approach that combines complementary spaceborne remote sensing sensors, namely …

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High resolution conductivity mapping using regional AEM survey and machine learning.

Geoscience Australia Data · Updated 16 Oct 2025

… We have used a machine learning modelling approach that establishes predictive relationships between the inverted flight-line modelled …

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TEAMER: Field Demonstration of MarineSitu Marine Energy Monitoring Tools (Public)

MarineSitu · Updated 28 Aug 2026

… Machine Learning Artifacts: Object detection model weights (.pt) for three iterations each of optical and acoustic models, validation …

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Morphological map of the Irish continental shelf created using Deep Learning

Marine Institute Ireland · Updated 31 Jul 2024

… qualitatively and quantitatively best Fully Convolutional Neural Networks models obtained in the study: Arosio, R., Hobley, B., Wheeler …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 18 Nov 2025

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 8 Jan 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 20 Jan 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 2 Mar 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 7 Mar 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 25 Mar 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 20 Apr 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Geoscience Australia Data · Updated 30 Apr 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Australian Ocean Data Network · Updated 20 Dec 2025

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Australian Ocean Data Network · Updated 11 Jan 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

• HTML

Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Australian Ocean Data Network · Updated 23 Jan 2026

… 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

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