Deep learning models, which imitate the function of human brain, have drawn attention from many engineering fields (mechanical, agricultural, and computer engineering etc). The major advantages of deep learning in engineering fields can be summarized by objects detection, classification, and time-series prediction. As well, it has been applied into environmental science and engineering fields. Here, we compiled our previous attempts to apply deep learning models in water-environment field and presented the future opportunities.

ABSTRACT
1. 서 론
2. 적용 사례
    2.1 딥러닝 기법을 이용한 유해 남조류 자동판별과 개체 수 산정
    2.2 딥러닝 기법을 이용한 초분광 영상처리를 통한조류 농도 산정
    2.3 딥러닝 기법을 이용한 막오염 및 수투과도 예측
    2.4 딥러닝 기법을 이용한 토양 내 중금속 농도 산정
3. 결 론
References

• 표종철(울산과학기술원 도시환경공학부) | Jongcheol Pyo (Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
• 박상훈(울산과학기술원 도시환경공학부) | Sanghun Park (Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
• 조경화(울산과학기술원 도시환경공학부) | Kyung-Hwa Cho (Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
• 백상수(울산과학기술원 도시환경공학부) | Sang-Soo Baek (Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) Corresponding author