Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches

Jhemson Brédy, Jacques Gallichand, Paul Celicourt and Silvio José Gumiere

Agricultural Water Management, 2020, vol. 233, issue C

Abstract: Integrated groundwater management is a major challenge for industrial, agricultural and domestic activities. In some agricultural production systems, optimized water table management represents a significant factor to improve crop yields and water use. Therefore, predicting water table depth (WTD) becomes an important means to enable real-time planning and management of groundwater resources. This study proposes a decision-tree-based modelling approach for WTD forecasting as a function of precipitation, previous WTD values and evapotranspiration with applications in groundwater resources management for cranberry farming. Firstly, two decision-tree-based models, namely Random Forest (RF) and Extreme Gradient Boosting (XGB), were parameterized and compared to predict the WTD up to 48 -h ahead for a cranberry farm located in Québec, Canada. Secondly, the importance of the predictor variables was analyzed to determine their influence on WTD simulation results. WTD measurements at three observation wells within a cranberry field, for the growing period from July 8, 2017 to August 30, 2017, were used for training and testing the models. Statistical parameters such as the mean squared error, coefficient of determination and Nash-Sutcliffe Efficiency coefficient were used to measure models performance. The results show that the XGB model outperformed the RF model for all predictions of WTD and was, accordingly, selected as the optimal model. Among the predictor variables, the antecedent WTD was the most important for water table depth simulation, followed by the precipitation. Based on the most important variables and optimal model, the prediction error for entire WTD range was within ±5 cm for 1-, 12-, 24-, 36- and 48 -h predictions. The XGB models can provide useful information on the WTD dynamics and a rigorous simulation for irrigation planning and management in cranberry fields.

Keywords: Random forest; Extreme gradient boosting; Machine learning; Groundwater level; Evapotranspiration; Precipitation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:233:y:2020:i:c:s0378377419319420

DOI: 10.1016/j.agwat.2020.106090

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