Flood Forecasting Using Machine Learning Methods
| dc.contributor.author | Chang, Fi-John | * |
| dc.contributor.author | Hsu, Kuolin | * |
| dc.contributor.author | Chang, Li-Chiu | * |
| dc.date.accessioned | 2021-02-11T13:47:09Z | |
| dc.date.available | 2021-02-11T13:47:09Z | |
| dc.date.issued | 2019 | * |
| dc.date.submitted | 2019-03-08 11:42:05 | * |
| dc.identifier | 32461 | * |
| dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/47751 | |
| dc.description.abstract | This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water | * |
| dc.language | English | * |
| dc.subject | TA1-2040 | * |
| dc.subject | T1-995 | * |
| dc.subject | TA170-171 | * |
| dc.subject.classification | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology | en_US |
| dc.subject.other | natural hazards & | * |
| dc.subject.other | artificial neural network | * |
| dc.subject.other | flood routing | * |
| dc.subject.other | the Three Gorges Dam | * |
| dc.subject.other | backtracking search optimization algorithm (BSA) | * |
| dc.subject.other | lag analysis | * |
| dc.subject.other | artificial intelligence | * |
| dc.subject.other | classification and regression trees (CART) | * |
| dc.subject.other | decision tree | * |
| dc.subject.other | real-time | * |
| dc.subject.other | optimization | * |
| dc.subject.other | ensemble empirical mode decomposition (EEMD) | * |
| dc.subject.other | improved bat algorithm | * |
| dc.subject.other | convolutional neural networks | * |
| dc.subject.other | ANFIS | * |
| dc.subject.other | method of tracking energy differences (MTED) | * |
| dc.subject.other | adaptive neuro-fuzzy inference system (ANFIS) | * |
| dc.subject.other | recurrent nonlinear autoregressive with exogenous inputs (RNARX) | * |
| dc.subject.other | disasters | * |
| dc.subject.other | flood prediction | * |
| dc.subject.other | ANN-based models | * |
| dc.subject.other | flood inundation map | * |
| dc.subject.other | ensemble machine learning | * |
| dc.subject.other | flood forecast | * |
| dc.subject.other | sensitivity | * |
| dc.subject.other | hydrologic models | * |
| dc.subject.other | phase space reconstruction | * |
| dc.subject.other | water level forecast | * |
| dc.subject.other | data forward prediction | * |
| dc.subject.other | early flood warning systems | * |
| dc.subject.other | bees algorithm | * |
| dc.subject.other | random forest | * |
| dc.subject.other | uncertainty | * |
| dc.subject.other | soft computing | * |
| dc.subject.other | data science | * |
| dc.subject.other | hydrometeorology | * |
| dc.subject.other | LSTM | * |
| dc.subject.other | rating curve method | * |
| dc.subject.other | forecasting | * |
| dc.subject.other | superpixel | * |
| dc.subject.other | particle swarm optimization | * |
| dc.subject.other | high-resolution remote-sensing images | * |
| dc.subject.other | machine learning | * |
| dc.subject.other | support vector machine | * |
| dc.subject.other | Lower Yellow River | * |
| dc.subject.other | extreme event management | * |
| dc.subject.other | runoff series | * |
| dc.subject.other | empirical wavelet transform | * |
| dc.subject.other | Muskingum model | * |
| dc.subject.other | hydrograph predictions | * |
| dc.subject.other | bat algorithm | * |
| dc.subject.other | data scarce basins | * |
| dc.subject.other | Wilson flood | * |
| dc.subject.other | self-organizing map | * |
| dc.subject.other | big data | * |
| dc.subject.other | extreme learning machine (ELM) | * |
| dc.subject.other | hydroinformatics | * |
| dc.subject.other | nonlinear Muskingum model | * |
| dc.subject.other | invasive weed optimization | * |
| dc.subject.other | rainfall–runoff | * |
| dc.subject.other | flood forecasting | * |
| dc.subject.other | artificial neural networks | * |
| dc.subject.other | flash-flood | * |
| dc.subject.other | streamflow predictions | * |
| dc.subject.other | precipitation-runoff | * |
| dc.subject.other | the upper Yangtze River | * |
| dc.subject.other | survey | * |
| dc.subject.other | parameters | * |
| dc.subject.other | Haraz watershed | * |
| dc.subject.other | ANN | * |
| dc.subject.other | time series prediction | * |
| dc.subject.other | postprocessing | * |
| dc.subject.other | flood susceptibility modeling | * |
| dc.subject.other | rainfall-runoff | * |
| dc.subject.other | deep learning | * |
| dc.subject.other | database | * |
| dc.subject.other | LSTM network | * |
| dc.subject.other | ensemble technique | * |
| dc.subject.other | hybrid neural network | * |
| dc.subject.other | self-organizing map (SOM) | * |
| dc.subject.other | data assimilation | * |
| dc.subject.other | particle filter algorithm | * |
| dc.subject.other | monthly streamflow forecasting | * |
| dc.subject.other | Dongting Lake | * |
| dc.subject.other | machine learning methods | * |
| dc.subject.other | micro-model | * |
| dc.subject.other | stopping criteria | * |
| dc.subject.other | Google Maps | * |
| dc.subject.other | cultural algorithm | * |
| dc.subject.other | wolf pack algorithm | * |
| dc.subject.other | flood events | * |
| dc.subject.other | urban water bodies | * |
| dc.subject.other | Karahan flood | * |
| dc.subject.other | St. Venant equations | * |
| dc.subject.other | hybrid & | * |
| dc.subject.other | hydrologic model | * |
| dc.title | Flood Forecasting Using Machine Learning Methods | * |
| dc.type | book | |
| oapen.identifier.doi | 10.3390/books978-3-03897-549-6 | * |
| oapen.relation.isPublishedBy | 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 | * |
| oapen.relation.isbn | 9783038975489 | * |
| oapen.pages | 376 | * |
| oapen.edition | 1st | * |
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