Advanced machine learning-based kharif maize evapotranspiration estimation in semi-arid climate

Abstract

Accurate Crop Evapotranspiration (ETc) estimation is crucial for understanding hydrological and agrometeorological processes, yet it's challenged by multiple parameters, data variations, and lack of continuity. These limitations restrict numerical methods application. To address this, the study aims to develop and assess ML models for daily maize ETc in semi-arid areas, utilizing varied weather inputs. Five ML models viz., Category Boosting (CB), Linear Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD) were developed and validated for the ICAR-IARI, New Delhi, Research Station. Penman-Monteith (PM) model estimated ETc values are used as the standard for comparing the performance of the ML model values. Results revealed that the SVM model achieved the highest coefficient of determination (R2) among all models, with a value of 0.987. Furthermore, the SVM model exhibited the lowest model errors (MAE = 0.121 mm day−1, RMSE = 0.172 mm day−1, and MAPE = 4.37%) compared to other models. The ANN model also demonstrated promising results, comparable to the SVM model. Notably, the wind speed parameter was found most influential input parameter. In conclusion, SVM or ANN could be considered reliable alternative methods for the accurate estimation of kharif maize crop ETc in the semi-arid climate. HIGHLIGHTS Five machine learning algorithm-based models were developed and evaluated to estimate daily kharif maize ETc.; Five instances were created using limited and full weather inputs for estimating daily kharif maize ETc.; Regression analysis was performed to determine the parameters that had a significant impact on ETc estimation and cross-correlation analysis for instance creation.; Multicriteria decision-making (MCDM)-based Simple Additive weighting (SAW) method was used to rank and select the best model.; The SVM- and ANN-based model demonstrated satisfactory accuracy for estimating maize ETc in semi-arid climatic conditions.;

Keywords

• category boosting
• crop coefficient
• crop evapotranspiration
• cross-correlation
• machine learning
• simple additive weighting