Flood Forecasting Using Machine Learning Methods
This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water
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| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 electronic resource (376 p.) |
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| 100 | 1 | |a Chang, Fi-John |4 auth | |
| 245 | 1 | 0 | |a Flood Forecasting Using Machine Learning Methods |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (376 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 520 | |a This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water | ||
| 546 | |a English | ||
| 653 | |a natural hazards & | ||
| 653 | |a artificial neural network | ||
| 653 | |a flood routing | ||
| 653 | |a the Three Gorges Dam | ||
| 653 | |a backtracking search optimization algorithm (BSA) | ||
| 653 | |a lag analysis | ||
| 653 | |a artificial intelligence | ||
| 653 | |a classification and regression trees (CART) | ||
| 653 | |a decision tree | ||
| 653 | |a real-time | ||
| 653 | |a optimization | ||
| 653 | |a ensemble empirical mode decomposition (EEMD) | ||
| 653 | |a improved bat algorithm | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a ANFIS | ||
| 653 | |a method of tracking energy differences (MTED) | ||
| 653 | |a adaptive neuro-fuzzy inference system (ANFIS) | ||
| 653 | |a recurrent nonlinear autoregressive with exogenous inputs (RNARX) | ||
| 653 | |a disasters | ||
| 653 | |a flood prediction | ||
| 653 | |a ANN-based models | ||
| 653 | |a flood inundation map | ||
| 653 | |a ensemble machine learning | ||
| 653 | |a flood forecast | ||
| 653 | |a sensitivity | ||
| 653 | |a hydrologic models | ||
| 653 | |a phase space reconstruction | ||
| 653 | |a water level forecast | ||
| 653 | |a data forward prediction | ||
| 653 | |a early flood warning systems | ||
| 653 | |a bees algorithm | ||
| 653 | |a random forest | ||
| 653 | |a uncertainty | ||
| 653 | |a soft computing | ||
| 653 | |a data science | ||
| 653 | |a hydrometeorology | ||
| 653 | |a LSTM | ||
| 653 | |a rating curve method | ||
| 653 | |a forecasting | ||
| 653 | |a superpixel | ||
| 653 | |a particle swarm optimization | ||
| 653 | |a high-resolution remote-sensing images | ||
| 653 | |a machine learning | ||
| 653 | |a support vector machine | ||
| 653 | |a Lower Yellow River | ||
| 653 | |a extreme event management | ||
| 653 | |a runoff series | ||
| 653 | |a empirical wavelet transform | ||
| 653 | |a Muskingum model | ||
| 653 | |a hydrograph predictions | ||
| 653 | |a bat algorithm | ||
| 653 | |a data scarce basins | ||
| 653 | |a Wilson flood | ||
| 653 | |a self-organizing map | ||
| 653 | |a big data | ||
| 653 | |a extreme learning machine (ELM) | ||
| 653 | |a hydroinformatics | ||
| 653 | |a nonlinear Muskingum model | ||
| 653 | |a invasive weed optimization | ||
| 653 | |a rainfall–runoff | ||
| 653 | |a flood forecasting | ||
| 653 | |a artificial neural networks | ||
| 653 | |a flash-flood | ||
| 653 | |a streamflow predictions | ||
| 653 | |a precipitation-runoff | ||
| 653 | |a the upper Yangtze River | ||
| 653 | |a survey | ||
| 653 | |a parameters | ||
| 653 | |a Haraz watershed | ||
| 653 | |a ANN | ||
| 653 | |a time series prediction | ||
| 653 | |a postprocessing | ||
| 653 | |a flood susceptibility modeling | ||
| 653 | |a rainfall-runoff | ||
| 653 | |a deep learning | ||
| 653 | |a database | ||
| 653 | |a LSTM network | ||
| 653 | |a ensemble technique | ||
| 653 | |a hybrid neural network | ||
| 653 | |a self-organizing map (SOM) | ||
| 653 | |a data assimilation | ||
| 653 | |a particle filter algorithm | ||
| 653 | |a monthly streamflow forecasting | ||
| 653 | |a Dongting Lake | ||
| 653 | |a machine learning methods | ||
| 653 | |a micro-model | ||
| 653 | |a stopping criteria | ||
| 653 | |a Google Maps | ||
| 653 | |a cultural algorithm | ||
| 653 | |a wolf pack algorithm | ||
| 653 | |a flood events | ||
| 653 | |a urban water bodies | ||
| 653 | |a Karahan flood | ||
| 653 | |a St. Venant equations | ||
| 653 | |a hybrid & | ||
| 653 | |a hydrologic model | ||
| 776 | |z 3-03897-548-6 | ||
| 700 | 1 | |a Hsu, Kuolin |4 auth | |
| 700 | 1 | |a Chang, Li-Chiu |4 auth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 06:02:49 Europe/Vienna |f system |c marc21 |a 2019-11-10 04:18:40 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338779350004498&Force_direct=true |Z 5338779350004498 |b Available |8 5338779350004498 | ||