With the current trend of the fourth industrial revolution, machine learning technique is increasingly adopted in various water industry fields. In this review paper, recent studies using machine learning to predict flood, water consumption, water quality, and water treatment processes are summarized. In the typical water purification processes such as flocculation, disinfection, and filtration, machine learning was able to present high-accuracy prediction results for complex non-linear mechanisms. Hybrid machine learning methods, combining multiple algorithms, generally outperformed machine learning results using only one algorithm. A more microscopic machine learning approach can provide valuable information to the operators in the water industry.

ABSTRACT
1. 서 론
2. 자료 수집 방법
3. 머신러닝 개요 및 주요 방법론
    3.1 머신러닝 개요
    3.2 머신러닝 알고리즘
    3.3 하이브리드 및 앙상블 방법론
4. 물 산업 기반 머신러닝 연구 동향
    4.1 홍수 예측 분야
    4.2 물 사용량 분야
    4.3 수질 예측
    4.4 정수처리 공정에서의 머신러닝
5. 결 론
기호 설명
References

• 오원기(한국산업기술시험원 디지털혁신센터) | Wonki O (Digital Innovation Center, Korea Testing Laboratory)
• 장해남(경상국립대학교 에너지공학과) | Haenam Jang (Department of Energy Engineering, Future Convergence Technology Research Institute, Gyeongsang National University) Corresponding author
• 신승구(경상국립대학교 에너지공학과) | Seung Gu Shin (Department of Energy Engineering, Future Convergence Technology Research Institute, Gyeongsang National University)