Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches
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DOI: 10.1016/j.agwat.2020.106090
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- Jamei, Mehdi & Sharma, Prabhakar & Ali, Mumtaz & Bora, Bhaskor J. & Malik, Anurag & Paramasivam, Prabhu & Farooque, Aitazaz A. & Abdulla, Shahab, 2024. "Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine," Energy, Elsevier, vol. 288(C).
- Soyoung Park & Jinsoo Kim, 2021. "The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential," Sustainability, MDPI, vol. 13(5), pages 1-19, February.
- Sebastian Gutierrez Pacheco & Robert Lagacé & Sandrine Hugron & Stéphane Godbout & Line Rochefort, 2021. "Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
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Keywords
Random forest; Extreme gradient boosting; Machine learning; Groundwater level; Evapotranspiration; Precipitation;All these keywords.
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