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Random Forest Prediction Algorithm for Eastern Africa, 1991-2023.

Domaine:

climateenvironment and energy

Type de record:

software
Créateur:
Zoe
Éditeur:
Zenodo
Hôte:avatar

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) products are multi-satellite products containing microwave sensor information, which provides soil moisture (SM) information. Four products are available: The active (scatterometer), passive (radiometer), combined (sensor fusion), and gap-filled (interpolated) datasets.

Based on this data and key variables, like temperature, elevation, or precipitation, SM can be predicted. This algorithm specificially allows the prediction of SM for Eastern Africa from 1991-2023, as well as the computation of spatial cross-validation and performance metrics.

Visit

doi.org

Licenses

info:eu-repo/semantics/openAccessMIT Licensehttps://opensource.org/licenses/MIT

Similaires

Soil Moisture Prediction for Eastern Africa 1991-2023 [Part 2]: Combined and Gap-filled Random Forest Replication Files.Soil Moisture Prediction for Eastern Africa 1991-2023: Sharing ESA CCI SM Processsed Datasets and Key Predictors, and Random Forest Replication Files.Soil Moisture Prediction for Eastern Africa 1991-2023: Sharing ESA CCI SM Processed Datasets and Key Predictors, and Random Forest Replication Files.Validation Algorithm for Ground Station and RF models for Eastern Africa, 1991-2023.Results for random forest prediction.Rockburst Intensity Prediction Based on African Vultures Optimization Algorithm-Random Forest Model

Soil Moisture Prediction for Eastern Africa 1991-2023 [Part 2]: Combined and Gap-filled Random Forest Replication Files.

The attached datasets enable the prediction of soil moisture (SM) over Eastern Africa from 1991-2023

Soil Moisture Prediction for Eastern Africa 1991-2023: Sharing ESA CCI SM Processsed Datasets and Key Predictors, and Random Forest Replication Files.

The attached datasets enable the prediction of soil moisture (SM) over Eastern Africa from 1991-2023

Soil Moisture Prediction for Eastern Africa 1991-2023: Sharing ESA CCI SM Processed Datasets and Key Predictors, and Random Forest Replication Files.

The attached datasets enable the prediction of soil moisture (SM) over Eastern Africa from 1991-2023

Validation Algorithm for Ground Station and RF models for Eastern Africa, 1991-2023.

This algorithm allows the validation process of the previously established RF models and in

Results for random forest prediction.

A random forest model based on taxon discrimination by monthly rainfall (Jan-Dec) was used to pre

Rockburst Intensity Prediction Based on African Vultures Optimization Algorithm-Random Forest Model