AI-Driven Ecohydrology: Monitoring, Modeling, and Mitigating Seawater Intrusion, Compound Flooding, and Saline Soils in Coastal Wetlands
AI-Driven Ecohydrology: Monitoring, Modeling, and Mitigating Seawater Intrusion, Compound Flooding, and Saline Soils in Coastal Wetlands
Session ID#: 282107
Session Description:
This session focuses on integrated approaches to address the growing challenges in coastal regions posed by seawater intrusion, compound flooding, and soil salinization. We welcome contributions that highlight AI-driven modeling, knowledge-guided machine learning, advanced in situ sensors, remote sensing, and big geospatial data to improve our understanding of coastal ecohydrology, particularly in wetlands and reclaimed saline soils. By combining novel data-fusion techniques with cutting-edge process-based research, we aim to enhance predictive capabilities and inform sustainable nature resource management strategies. Submissions featuring interdisciplinary studies, as well as new methodological developments, are encouraged.
Co-Sponsor(s):
- B - Biogeosciences
- IN - Informatics
- NH - Natural Hazards
Index Terms:
1402 - Critical Zone [CRITICAL ZONE]
1829 Groundwater hydrology [HYDROLOGY]
1865 Soils [HYDROLOGY]
1890 Wetlands [HYDROLOGY]
Primary Convener: Yao Hu, University of Delaware, Department of Geography & Spatial Sciences; Civil, Construction, and Environmental Engineering, Newark, DE, United States
Conveners: Wenhong Li, Duke Univ-Nicholas School, Durham, United States and Jingyi Huang, University of Wisconsin-Madison, Soil and Environmental Sciences, Madison, United States
See more of: Hydrology