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머신러닝 기법을 활용한 논 순용수량 예측

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

배승종|장민원|김수진
한국농촌계획학회 (Korean Society Of Rural Planning)|2022.11|농촌계획 (Journal of Korean Society of Rural Planning)|제28권 제4호|pp. 105-117

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참고문헌

키워드

machine learningIrrigation water requirementpaddy fieldrandom forestartificial neural network
주제분류농학

초록

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machinelearning 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   of the CEP model was higher, and MAE, RMSE, and MSE were l ower. Comprehensivel y 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.

Abstract

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machinelearning 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   of the CEP model was higher, and MAE, RMSE, and MSE were l ower. Comprehensivel y 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.

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