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...

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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
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Summary: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 learning models (XGB, RF, LASSO, and CatBoost) and eight hybrid machine learning models (XGB-RF, XGB-LASSO, XGB-CatBoost, RF-LASSO, CatBoost-LASSO, CatBoost-RF, XGB-RF-LASSO, and XGB-CatBoost-LASSO) were used, along with stacking ensembles, with five scenarios including climate and crop parameters and remote sensing-based indices. The highest R<sup>2</sup> value for predicting wheat BWFP was achieved with XGB-LASSO under scenario 4 at 100%, while the minimum was 0.16 with LASSO under scenario 3 (remote sensing indices). To predict wheat GWFP, the highest R<sup>2</sup> value of 100% was achieved with RF-LASSO across scenario 1 (all parameters), scenario 2 (climate parameters), scenario 4 (Pe<sub>eff</sub>, T<sub>max</sub>, T<sub>min</sub>, and SA), and scenario 5 (Pe<sub>eff</sub> and T<sub>max</sub>). The lowest value was recorded with LASSO and scenario 3. The use of individual and hybrid machine learning models showed high efficiency in predicting the blue and green water footprint of wheat, with high ratings according to statistical performance standards. However, the hybrid programs, whether binary or triple, outperformed both the single models and stacking ensemble.
ISSN:2072-4292

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