Forecasting Blue and Green Water Footprint of Wheat Based on Single, Hybrid, and Stacking Ensemble Machine Learning Algorithms Under Diverse Agro-Climatic Conditions in Nile Delta, Egypt

Forecasting Blue and Green Water Footprint of Wheat Based on Single, Hybrid, and Stacking Ensemble Machine Learning Algorithms Under Diverse Agro-Climatic Conditions in Nile Delta, Egypt

The aim of this research is to develop and compare single, hybrid, and stacking ensemble machine learning models under spatial and temporal climate variations in the Nile Delta regarding the estimation of the blue and green water footprint (BWFP and GWFP) for wheat. Thus, four single machine learnin...

Full description

Saved in:
Bibliographic Details
Main Authors: Ashrakat A. Lotfy, Mohamed E. Abuarab, Eslam Farag, Bilal Derardja, Roula Khadra, Ahmed A. Abdelmoneim, Ali Mokhtar
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Remote Sensing
Subjects:
wheat BWFP and GWFP
climatic parameters
remote sensing indices
machine learning models
single and hybrid models
stacking ensemble
Online Access:https://www.mdpi.com/2072-4292/16/22/4224
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2072-4292/16/22/4224

Similar Items