ARIMA 모델을 이용한 수막재배지역 지하수위 시계열 분석 및 미래추세 예측
Time-series Analysis and Prediction of Future Trends of Groundwater Level in Water Curtain Cultivation Areas Using the ARIMA Model
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초록
This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhousecomplexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to comparethe groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series databy the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain’s tworepresentative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain andmountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to onemodel due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore,it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwaterforecasting results through time series analysis can be used for sustainable groundwater resource management.
Abstract
This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhousecomplexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to comparethe groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series databy the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain’s tworepresentative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain andmountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to onemodel due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore,it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwaterforecasting results through time series analysis can be used for sustainable groundwater resource management.
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