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| DOI：10.13522/j.cnki.ggps.2025348 |
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| A low-cost method for estimating electricity-to-water conversion coefficients for well-irrigated areas without metering facilities |
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Guo Ning, Xiang Yao, Zuo Ganggang
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1. Dizhang Subdistrict Office, Xixian New Area, Xi’an 712035, China; 2. State Key Laboratory of Water Engineering
Ecology and Environment in Arid Area, Xi’an University of Technology, Xi’an 710048, China
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| Abstract: |
| 【Objective】In well-irrigated districts, accurately metering water pumped from individual boreholes is challenging because of their dispersed distribution and high costs of meter installation and maintenance. This paper proposes a low-cost and efficient method for estimating groundwater pumped for irrigation in areas without metering facilities.【Method】Measured electricity-to-water conversion coefficients and factors that affect the conversion coefficients were collected across multiple regions. These factors included well age and depth, groundwater table depth, pump model and age, and irrigation type. Missing data were imputed using an XGBoost-based chained imputation method. Random Forest and LightGBM were used as benchmark model for predicting the conversion coefficient, and an optimized XGBoost model was developed by integrating the Optuna automated hyperparameter optimization framework with stratified K-fold cross-validation. Statistical significance tests were conducted to compare model performance; permutation importance with error bars was used to quantitatively identify the key factors that influence the electricity- to-water conversion coefficient.【Result】①The XGBoost chained imputation method effectively captured nonlinear interactions among variables while maintaining high physical consistency of the imputed data. ②The XGBoost model achieved a Nash-Sutcliffe efficiency (NSE) of up to 0.987 on the test set, significantly outperforming the LightGBM and Random Forest models. ③Statistical tests showed that XGBoost significantly outperformed LightGBM and was more robust than Random Forest under extreme conditions, such as high pumping lift. ④Actual pumping lift, rated pump lift, and rated flow rate were the most influential factors affecting the electricity-to-water conversion coefficient.【Conclusion】The proposed imputation-optimization-prediction framework effectively addresses the challenges associated with missing data and demonstrates the ability of XGBoost to capture nonlinear relationships among irrigation-related variables. Our results show that the actual pumping lift is the most important factor for data collection; they provide a generalizable, low-cost approach for estimating agricultural groundwater use for irrigation in areas without metering facilities. |
| Key words: electricity-to-water conversion irrigation by motor-pumped wells XGBoost model missing data handling machine learning |
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