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http://dx.doi.org/10.7851/ksrp.2022.28.4.105

Prediction of Net Irrigation Water Requirement in paddy field Based on Machine Learning  

Kim, Soo-Jin (Institute of Green Bio Science and Technology, Seoul National University)
Bae, Seung-Jong (Institute of Green Bio Science and Technology, Seoul National University)
Jang, Min-Won (Department of Agricultural Engineering, Institute of Agriculture and Life Science, Gyeongsang National University)
Publication Information
Journal of Korean Society of Rural Planning / v.28, no.4, 2022 , pp. 105-117 More about this Journal
Abstract
This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R2 of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.
Keywords
Irrigation water requirement; paddy field; machine learning; random forest; artificial neural network;
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