pzywczuk/n2o-ssa-models: Nitrous oxide emissions across Sub-Saharan Africa: meta-analysis and data-driven modelling

Domain:

environment and energyclimateagriculture

Record type:

softwaremodel

Creator:

Phi

Publisher:

Host:

This repository contains three machine learning models for predicting N2O emissions from different land use types (Forest, Cropland, Grassland) using environmental and management variables.

Models Included

Random Forest Model (random_forest_n2o_model.py)
Artificial Neural Network Model (artificial_neural_network_n2o_model.py)
XGBoost Model (xgboost_n2o_model.py)

Visit

doi.org

Licenses

info:eu-repo/semantics/openAccess MIT License https://opensource.org/licenses/MIT

Nitrous oxide emissions across Sub-Saharan Africa: meta-analysis and data-driven modelling

This comprehensive dataset collection presents nitrous oxide (N2O) flux measurements from 27 sites a

Nitrous Oxide Emissions Across Sub‐Saharan Africa: Meta‐Analysis and Data‐Driven Modeling

Abstract Food security and avoiding land use change in Sub‐Saharan Africa (SSA)

pzywczuk/n2o-ssa-models

Machine learning models (Random Forest, XGBoost, Feedforward Neural Network) for predicting N₂O emis

Nitrous Oxide Emissions from Smallholders’ Cropping Systems in Sub-Saharan Africa

Increased concentration of atmospheric nitrous oxide (N2O), a potent greenhouse gas (GHG), is of gre