Water resources planning and management are, more and more, becoming important issue for water use and flood control due to the population increase, urbanization, and climate change. In particular, the estimating and the forecasting inflow of dam is the most important hydrologic issue for flood control and reliable water supply. Therefore, this study forecasted monthly inflow of Soyang river dam using VARMA model and 3 machine learning models. The forecasting models were constructed using monthly inflow data in the period of 1974 to 2016 and then the inflows were forecasted at 12- and 24-month ahead lead times. As a result, the forecasted monthly inflows by the models mostly were less than the observed ones, but the peak time and the variation pattern were well forecasted. Especially, the VARMA model showed very good performance in the forecasting. Therefore, the result of this study indicates that the VARMA model can be used efficiently to forecast hydrologic data and also used to establish water supply and management plan.

Abstract
1. 서론
2. 연구대상 유역 및 월유입량 자료 수집
    1) 대상유역 및 기후특성
    2) 소양강댐 월유입량 자료 및 기본통계 분석
3. VARMA 모형 및 머신러닝 모형
    1) 상태공간 모형 및 VARMA 모형
    2) 머신러닝 모형
4. 소양강댐 월유입량 예측모형구축 및 예측력 평가
    1) VARMA 모형에 의한 월유입량 예측
    2) 머신러닝 모형에 의한 월유입량 예측
    3) VARMA 모형과 머신러닝 모형의 예측력 평가
5. 결론
References

• 배영혜(인하대학교 토목공학과) | Younghye Bae (Department of Civil Engineering, Inha University)
• 김종성(인하대학교 토목공학과) | Jongsung Kim (Department of Civil Engineering, Inha University)
• 왕원준((사)방재관리연구센터) | Wonjoon Wang (Disaster Management Research Center) Correspondence
• 유영훈(인하대학교 토목공학과) | Younghoon Yoo (Department of Civil Engineering, Inha University)
• 정재원(인하대학교 토목공학과) | Jaewon Jung (Department of Civil Engineering, Inha University)
• 김형수(인하대학교 토목공학과) | Hung Soo Kim (Department of Civil Engineering, Inha University)