Version 1
: Received: 5 July 2024 / Approved: 5 July 2024 / Online: 5 July 2024 (12:09:35 CEST)
How to cite:
Kumar, M.; Agrawal, Y.; Adamala, S.; ., P.; A.V.M, S.; Singh, V.; Srivastava, A. Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation. Preprints2024, 2024070535. https://doi.org/10.20944/preprints202407.0535.v1
Kumar, M.; Agrawal, Y.; Adamala, S.; ., P.; A.V.M, S.; Singh, V.; Srivastava, A. Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation. Preprints 2024, 2024070535. https://doi.org/10.20944/preprints202407.0535.v1
Kumar, M.; Agrawal, Y.; Adamala, S.; ., P.; A.V.M, S.; Singh, V.; Srivastava, A. Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation. Preprints2024, 2024070535. https://doi.org/10.20944/preprints202407.0535.v1
APA Style
Kumar, M., Agrawal, Y., Adamala, S., ., P., A.V.M, S., Singh, V., & Srivastava, A. (2024). Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation. Preprints. https://doi.org/10.20944/preprints202407.0535.v1
Chicago/Turabian Style
Kumar, M., V.K. Singh and Ankur Srivastava. 2024 "Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation" Preprints. https://doi.org/10.20944/preprints202407.0535.v1
Abstract
The potential of generalized machine learning models developed for crop water estimation was examined in the current study. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) are three ensembled machine learning models that were developed using all of the data from a single location from 1976 to 2017 and then immediately applied at eleven different locations without the need for any local calibration. For the test period of January 2018 to June 2020, the model's capacity to estimate the numerical values of crop water requirement (Pen-man-Monteith (PM) ETo values) was assessed. In comparison to the GBM and RF models, the XGBoost model outperformed them both marginally and significantly. The estimate's weighted standard error was smaller than 0.85 mm/day, and the model's effectiveness varied from 96% to 99% across various locations. The model's strong performance was indicated by the decreased noise-to-signal ratio. A real-time water management system at the regional level can be seamlessly linked with this type of model due to its accuracy in estimating crop water requirements and its capacity to generalize.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
