해안지역 홍수예측 및 위험관리를 위한 GIS-딥러닝 통합 시뮬레이션 체계 구축

• 영문명: An Integrated GIS and Deep Learning Simulation Framework for Coastal Flood Prediction and Risk Management
• 발행기관: 한국연안방재학회
• 저자명: 박진혁(Jin-Hyeok Park) 조미숙(Mi-Sook Cho) 권기룡(Ki-Ryong Kwon) 임학수(Hak-Soo Lim)
• 간행물 정보: 『한국연안방재학회지』제12권 제2호, 107~114쪽, 전체 8쪽
• 주제분류: 자연과학 > 지질학
• 파일형태: PDF
• 발행일자: 2025.06.30

국문 초록

This study proposes a GIS and deep learning-based integrated simulation framework to enhance the prediction and management of coastal flood risks. The framework integrates spatial datasets—including digital elevation models (DEMs), storm surge data, and terrain characteristics—preprocessed through ArcGIS Pro and Jupyter-based pipelines to estimate flood heights. A hybrid deep learning model, combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks in a parallel structure, is designed to learn spatial features and temporal dynamics simultaneously from time-series flood maps. The fused output is used to generate a probabilistic flood risk index. Experimental results using coastal datasets from Busan demonstrated that the hybrid model outperformed standalone CNN and LSTM models, achieving superior macro and weighted averages across multiple performance metrics. Notably, CNN exhibited perfect recall for early warnings, while LSTM showed the highest precision for policy decision support. The hybrid model balanced both strengths, proving robust even under imbalanced data conditions. The integration with ArcGIS Pro allows for high-resolution flood visualization and intuitive risk mapping, supporting real-time alerts and disaster response planning. This approach offers strong scalability to other coastal regions and practical utility for real-world early flood warning systems and climate resilience strategies.

목차

1. 서 론

2. 관련 연구

3. 제안 방법

4. 실험 결과

5. 결 론

References

키워드

Coastal flood risk GIS Deep learning Hybrid neural network Flood simulation

참고문헌

• International Journal of Advanced Computer Science and Applications
• Environment and Planning B: Urban Analytics and City Science
• Scientific Reports
• Water Resources Research
• Environmental Research Letters
• Water Science & Technology
• Water