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

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 22; p. 4224
• Main Authors: Lotfy, Ashrakat A., Abuarab, Mohamed E., Farag, Eslam, Derardja, Bilal, Khadra, Roula, Abdelmoneim, Ahmed A., Mokhtar, Ali
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Agricultural production; Algorithms; Climate change; Climate models; Climate prediction; Climatic conditions; climatic parameters; Data mining; Egypt; Food; Footprint analysis; Learning algorithms; Machine learning; machine learning models; Parameters; Population growth; Remote sensing; remote sensing indices; Sea level; single and hybrid models; stacking ensemble; Statistical models; Vegetation; Water; Water consumption; Water resources management; Water use; Wheat; wheat BWFP and GWFP; Egypt; Nile Delta; Ukraine; Russia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16224224

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 R2 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 R2 value of 100% was achieved with RF-LASSO across scenario 1 (all parameters), scenario 2 (climate parameters), scenario 4 (Peeff, Tmax, Tmin, and SA), and scenario 5 (Peeff and Tmax). 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.