Work

Enhancing reference crop evapotranspiration prediction in arid regions: A stacking ensemble learning approach for the Amu Darya basin

Year: 2025

Type: article

Abstract: Accurately predicting reference crop evapotranspiration ( ) is important for ensuring the sustainable use of water resources, especially in dry regions where increasing water scarcity, aridification, and sparse observations pose formidable challenges in estimating , like the Amu Darya Irrigation Basin (ADB). This study innovatively hybridized four supervised learning algorithms: Decision Trees (DT), Generalized Linear Models (GLM), K-Nearest Neighbours (KNN), and Support Vector Regression (SVR), to achieve an enhanced standalone stacking ensemble ( ) model that utilizes fewer inputs yet can be implemented across different prediction scenarios. Results showed that mean ET estimates are consistent with the FAO56-PM for cotton (3 mm day -1 ) and Sorghum (3 mm day -1 ), and a slight variance of 0.01 mm day -1 for rice. outperformed other models in daily prediction, with an optimal R² > 0.96, the lowest bias of 0.01 , RMSE: 0.65 and MAE: 0.42 . This indicates superior performance relative to DT (R²: 0.73, RMSE: 1.12 , and MAE: 0.86 ), GLM (R²: 0.85, RMSE: 0.83 , and MAE: 0.60 ), KNN (R²: 0.89, RMSE: 0.72 , and MAE: 0.46 ), and SVR (R²: 0.85, RMSE: 0.84 and MAE: 0.59 ). The relative uncertainty of in ADB was 10.44%, whereas KNN, SVR, GLM, and DT were 14.77%, 16.95%, 23.29%, and 34.49%, respectively. Moreover, has robust generalizability and can be operationalized to enable seamless predictions for unhindered access to daily estimates that are essential for improving irrigation scheduling and water use efficiency in cotton, rice, and sorghum production in ungauged arid croplands. The study highlights the importance of expanding the stacked generalization method to increase deeper understanding of its applicability across diverse dry ecological regions in the context of changing environmental conditions.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101554

Authors: Friday Uchenna Ochege, Xiuliang Yuan, Geping Luo

Institutions: University of Port Harcourt, Chinese Academy of Sciences, Xinjiang Institute of Ecology and Geography

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Climate action

Open Access status: gold

Estimated APC paid: $1,770