Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Random Forest Model for Agricultural and Hydrological Drought Analysis in Ethiopia (1982–2100)

Domain:

climateagricultureenvironment and energy

Record type:

modelsoftwaredataset
Creator:
Abd
Publisher:
Zenodo
Host:avatar

This dataset provides the Python implementation, GEE workflow, and trained Random Forest models used for agricultural and hydrological drought assessment in Ethiopia from 1982–2100. The work integrates ERA5-Land, FLDAS, CHIRPS, CHIRTS, and multi-model CMIP6 datasets (SSP245, SSP585) to analyze past and future drought dynamics.
The Random Forest model used for prediction of agricultural (SSMI/based) and hydrological (SRI-based) drought indices is openly available for reuse and adaptation.

Visit

doi.org

Languages

Ndasa

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similar

Workflows for Machine Learning Evaluation of Agricultural and Hydrological Drought Indicators (SSMI and SRI) in EthiopiaSpatiotemporal Assessment of Agricultural Drought in Semiarid Northern Nigeria Using MODIS Data and Explainable Random Forest (2000–2025)COMPARISON OF STATISTICAL MODEL AND RANDOM FOREST FOR GROUNDWATER CONTAMINATION PATTERNS.Random Forest model predicted land cover map for Gabon 2015.Random Forest model predicted land cover map for Liberia 2015.Variable importance from the Random Forest model.

Workflows for Machine Learning Evaluation of Agricultural and Hydrological Drought Indicators (SSMI and SRI) in Ethiopia

Spatiotemporal Assessment of Agricultural Drought in Semiarid Northern Nigeria Using MODIS Data and Explainable Random Forest (2000–2025)

COMPARISON OF STATISTICAL MODEL AND RANDOM FOREST FOR GROUNDWATER CONTAMINATION PATTERNS.

In this study, a random forest model was compared to a statistical model for predicting heavy metal

Random Forest model predicted land cover map for Gabon 2015.

Regional subsets showing different land cover classes across Gabon in finer detail are shown (A-D

Random Forest model predicted land cover map for Liberia 2015.

A-D insets are regional subsets showing different land cover classes across Liberia in finer deta

Variable importance from the Random Forest model.

Universal Health Coverage (UHC) is a global objective aimed at providing equitable access to