Susceptibility modeling of hydro-morphological processes considered river topology
Hydro-Morphological Processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are most likely to occur in small catchments, especially buffer zones along or near rivers. Rivers transfer matter and energy between hydrographic units, thus poten...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2440614 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Hydro-Morphological Processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are most likely to occur in small catchments, especially buffer zones along or near rivers. Rivers transfer matter and energy between hydrographic units, thus potentially affecting the occurrence of HMPs in nearby catchments. To date, previous HMP susceptibility studies based on data-driven modeling lacked taking into account these interactions between catchments. In this work, we fully considered the role played by river topology and developed a Topology-based HMP susceptibility model (Topo-HMPSM) to emulate the interactions between catchments and predict the susceptibility of HMPs for the Yangtze River Basin during 1985–2015. Results confirmed that our proposed model outperforms four selected baseline models with the best F1-score (mean = 0.744, best = 0.756) and relatively lower uncertainties. A graph-based deep neural network improves the predictive and interpretability of HMP susceptibility modeling using embedding learning techniques. This work attempts to set a standard for incorporating river topology into deep learning models. Our findings highlight the importance of river topology in predicting HMP and support better informed hazard mitigation strategies. |
|---|---|
| ISSN: | 1009-5020 1993-5153 |