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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2440614 |
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| author | Nan Wang Mingxiao Li Hongyan Zhang Weiming Cheng Chao Du Luigi Lombardo |
| author_facet | Nan Wang Mingxiao Li Hongyan Zhang Weiming Cheng Chao Du Luigi Lombardo |
| author_sort | Nan Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-91efcb1635364960bb5c08ae70dd9324 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-91efcb1635364960bb5c08ae70dd93242025-01-17T14:14:32ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532024-12-0112010.1080/10095020.2024.2440614Susceptibility modeling of hydro-morphological processes considered river topologyNan Wang0Mingxiao Li1Hongyan Zhang2Weiming Cheng3Chao Du4Luigi Lombardo5Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, ChinaKey Laboratory for Geo-Environmental Monitoring of Great Bay Area MNR, Shenzhen, ChinaKey Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, ChinaState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaGeography and Geoinformation Science, George Mason University, Fairfax, USAFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, NetherlandsHydro-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.https://www.tandfonline.com/doi/10.1080/10095020.2024.2440614Hydro-morphological processes (HMP)river topologydeep learningsusceptibilityThe Yangtze River Basin |
| spellingShingle | Nan Wang Mingxiao Li Hongyan Zhang Weiming Cheng Chao Du Luigi Lombardo Susceptibility modeling of hydro-morphological processes considered river topology Geo-spatial Information Science Hydro-morphological processes (HMP) river topology deep learning susceptibility The Yangtze River Basin |
| title | Susceptibility modeling of hydro-morphological processes considered river topology |
| title_full | Susceptibility modeling of hydro-morphological processes considered river topology |
| title_fullStr | Susceptibility modeling of hydro-morphological processes considered river topology |
| title_full_unstemmed | Susceptibility modeling of hydro-morphological processes considered river topology |
| title_short | Susceptibility modeling of hydro-morphological processes considered river topology |
| title_sort | susceptibility modeling of hydro morphological processes considered river topology |
| topic | Hydro-morphological processes (HMP) river topology deep learning susceptibility The Yangtze River Basin |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2024.2440614 |
| work_keys_str_mv | AT nanwang susceptibilitymodelingofhydromorphologicalprocessesconsideredrivertopology AT mingxiaoli susceptibilitymodelingofhydromorphologicalprocessesconsideredrivertopology AT hongyanzhang susceptibilitymodelingofhydromorphologicalprocessesconsideredrivertopology AT weimingcheng susceptibilitymodelingofhydromorphologicalprocessesconsideredrivertopology AT chaodu susceptibilitymodelingofhydromorphologicalprocessesconsideredrivertopology AT luigilombardo susceptibilitymodelingofhydromorphologicalprocessesconsideredrivertopology |