Resilient landscape pattern for reducing coastal flood susceptibility
Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a fl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Luo, Ziyuan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota - Wang, Meimei ELSEVIER, 2018, an international journal for scientific research into the environment and its relationship with man, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:856 ; year:2023 ; day:15 ; month:01 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.scitotenv.2022.159087 |
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| 520 | |a Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. | ||
| 520 | |a Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. | ||
| 700 | 1 | |a Tian, Jian |4 oth | |
| 700 | 1 | |a Zeng, Jian |4 oth | |
| 700 | 1 | |a Pilla, Francesco |4 oth | |
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10.1016/j.scitotenv.2022.159087 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001965.pica (DE-627)ELV059498641 (ELSEVIER)S0048-9697(22)06186-1 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Luo, Ziyuan verfasserin aut Resilient landscape pattern for reducing coastal flood susceptibility 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Tian, Jian oth Zeng, Jian oth Pilla, Francesco oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:856 year:2023 day:15 month:01 pages:0 https://doi.org/10.1016/j.scitotenv.2022.159087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 856 2023 15 0115 0 |
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10.1016/j.scitotenv.2022.159087 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001965.pica (DE-627)ELV059498641 (ELSEVIER)S0048-9697(22)06186-1 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Luo, Ziyuan verfasserin aut Resilient landscape pattern for reducing coastal flood susceptibility 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Tian, Jian oth Zeng, Jian oth Pilla, Francesco oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:856 year:2023 day:15 month:01 pages:0 https://doi.org/10.1016/j.scitotenv.2022.159087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 856 2023 15 0115 0 |
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10.1016/j.scitotenv.2022.159087 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001965.pica (DE-627)ELV059498641 (ELSEVIER)S0048-9697(22)06186-1 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Luo, Ziyuan verfasserin aut Resilient landscape pattern for reducing coastal flood susceptibility 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Tian, Jian oth Zeng, Jian oth Pilla, Francesco oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:856 year:2023 day:15 month:01 pages:0 https://doi.org/10.1016/j.scitotenv.2022.159087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 856 2023 15 0115 0 |
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10.1016/j.scitotenv.2022.159087 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001965.pica (DE-627)ELV059498641 (ELSEVIER)S0048-9697(22)06186-1 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Luo, Ziyuan verfasserin aut Resilient landscape pattern for reducing coastal flood susceptibility 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Tian, Jian oth Zeng, Jian oth Pilla, Francesco oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:856 year:2023 day:15 month:01 pages:0 https://doi.org/10.1016/j.scitotenv.2022.159087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 856 2023 15 0115 0 |
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10.1016/j.scitotenv.2022.159087 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001965.pica (DE-627)ELV059498641 (ELSEVIER)S0048-9697(22)06186-1 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Luo, Ziyuan verfasserin aut Resilient landscape pattern for reducing coastal flood susceptibility 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. Tian, Jian oth Zeng, Jian oth Pilla, Francesco oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:856 year:2023 day:15 month:01 pages:0 https://doi.org/10.1016/j.scitotenv.2022.159087 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 856 2023 15 0115 0 |
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Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. 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resilient landscape pattern for reducing coastal flood susceptibility |
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Resilient landscape pattern for reducing coastal flood susceptibility |
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Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. |
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Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. |
| abstract_unstemmed |
Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience. |
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