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Flood Forecasting Using Machine Learning Methods
| Flood Forecasting Using Machine Learning Methods |
| Autore | Chang Fi-John |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 online resource (376 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
adaptive neuro-fuzzy inference system (ANFIS)
ANFIS ANN ANN-based models artificial intelligence artificial neural network artificial neural networks backtracking search optimization algorithm (BSA) bat algorithm bees algorithm big data classification and regression trees (CART) convolutional neural networks cultural algorithm data assimilation data forward prediction data scarce basins data science database decision tree deep learning disasters Dongting Lake early flood warning systems empirical wavelet transform ensemble empirical mode decomposition (EEMD) ensemble machine learning ensemble technique extreme event management extreme learning machine (ELM) flash-flood flood events flood forecast flood forecasting flood inundation map flood prediction flood routing flood susceptibility modeling forecasting Google Maps Haraz watershed high-resolution remote-sensing images hybrid & hybrid neural network hydrograph predictions hydroinformatics hydrologic model hydrologic models hydrometeorology improved bat algorithm invasive weed optimization Karahan flood lag analysis Lower Yellow River LSTM LSTM network machine learning machine learning methods method of tracking energy differences (MTED) micro-model monthly streamflow forecasting Muskingum model natural hazards & nonlinear Muskingum model optimization parameters particle filter algorithm particle swarm optimization phase space reconstruction postprocessing precipitation-runoff rainfall-runoff random forest rating curve method real-time recurrent nonlinear autoregressive with exogenous inputs (RNARX) runoff series self-organizing map self-organizing map (SOM) sensitivity soft computing St. Venant equations stopping criteria streamflow predictions superpixel support vector machine survey the Three Gorges Dam the upper Yangtze River time series prediction uncertainty urban water bodies water level forecast Wilson flood wolf pack algorithm |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346688303321 |
Chang Fi-John
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Microwave Imaging and Electromagnetic Inverse Scattering Problems
| Microwave Imaging and Electromagnetic Inverse Scattering Problems |
| Autore | Di Donato Loreto |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (170 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
3D
5G communication adjoint inversion methods antenna array antenna testing array diagnosis Bayesian compressive sensing (BCS) breast cancer breast imaging compressed sensing contraction integral equation for inversion (CIE-I) contrast source inversion (CSI) contrast-source inversion discontinuous Galerkin method (DGM) electrical-property tomography electromagnetic inverse scattering electromagnetic inverse scattering problems finite-difference methods image-based approach imaging inverse obstacles problem inverse problems inverse scattering inverse source problem joint sparsity Kolmogorov-Smirnov (K-S) test linear sampling method magnetic resonance imaging microwave imaging microwave imaging profilometry microwave plasma diagnostics near-field measurements nonlinear optimization nonlinear problem orthogonality sampling method radar-based breast imaging rank minimization RCS estimation stopping criteria tomography |
| ISBN | 3-03921-951-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910674007403321 |
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Di Donato Loreto
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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