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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kenji Suzuki | |
dc.date.accessioned | 2022-05-02T08:28:06Z | - |
dc.date.available | 2022-05-02T08:28:06Z | - |
dc.date.issued | 2011 | |
dc.identifier.isbn | 9789533072203 | |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/146348 | - |
dc.language.iso | eng | |
dc.publisher | IntechOpen | |
dc.relation.isbasedon | 10.5772/2041 | |
dc.relation.uri | https://www.intechopen.com/books/artificial-neural-networks-industrial-and-control-engineering-applications | |
dc.rights.uri | CC BY-NC-ND (姓名標示-非商業性-禁止改作) | |
dc.source | DOAB | |
dc.subject.classification | Computer science | |
dc.subject.other | climate change | |
dc.subject.other | Canada | |
dc.subject.other | flooding frequency | |
dc.subject.other | catchment based macroscale floodplain model | |
dc.subject.other | uncertainty | |
dc.subject.other | flood inundation maps | |
dc.subject.other | climate change | |
dc.subject.other | EURO-CORDEX projections | |
dc.subject.other | continuous simulations | |
dc.subject.other | climate change | |
dc.subject.other | flood hazard | |
dc.subject.other | flood risk | |
dc.subject.other | return period | |
dc.subject.other | streamflow regulation rules | |
dc.subject.other | Canada | |
dc.subject.other | consecutive dry days | |
dc.subject.other | climate | |
dc.subject.other | downscaled projections | |
dc.subject.other | Southeast U.S. | |
dc.subject.other | CMIP5 | |
dc.subject.other | drought-flood abrupt alternation | |
dc.subject.other | temporal and spatial evolution | |
dc.subject.other | climate change | |
dc.subject.other | water quality | |
dc.subject.other | Copula function | |
dc.subject.other | spatial analog | |
dc.subject.other | extreme precipitation | |
dc.subject.other | future precipitation at urban scale | |
dc.subject.other | RCM uncertainty | |
dc.subject.other | flood frequency analysis | |
dc.subject.other | flash flood | |
dc.subject.other | climate change and variability | |
dc.subject.other | Boise River Watershed | |
dc.subject.other | HSPF | |
dc.subject.other | hydrological risk assessment | |
dc.subject.other | extreme hydrologic events | |
dc.subject.other | climate change impacts | |
dc.subject.other | downscaling | |
dc.subject.other | uncertainty | |
dc.subject.other | ensembles | |
dc.subject.other | water resource systems | |
dc.subject.other | frequency estimates | |
dc.subject.other | downscaling | |
dc.subject.other | future projections | |
dc.subject.other | RCP4.5 | |
dc.subject.other | RCP8.5 | |
dc.subject.other | changing of exceedance | |
dc.subject.other | Northeastern US | |
dc.subject.other | climate change | |
dc.subject.other | climate projections | |
dc.subject.other | extreme rainfall | |
dc.subject.other | floods | |
dc.subject.other | droughts | |
dc.title | Artificial Neural Networks - Industrial and Control Engineering Applications | |
dc.type | 電子教科書 | |
dc.classification | 自然科學類 | |
Theme: | 教科書-自然科學類 |
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