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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 electronic resource (376 p.) |
| Soggetto non controllato |
natural hazards &
artificial neural network flood routing the Three Gorges Dam backtracking search optimization algorithm (BSA) lag analysis artificial intelligence classification and regression trees (CART) decision tree real-time optimization ensemble empirical mode decomposition (EEMD) improved bat algorithm convolutional neural networks ANFIS method of tracking energy differences (MTED) adaptive neuro-fuzzy inference system (ANFIS) recurrent nonlinear autoregressive with exogenous inputs (RNARX) disasters flood prediction ANN-based models flood inundation map ensemble machine learning flood forecast sensitivity hydrologic models phase space reconstruction water level forecast data forward prediction early flood warning systems bees algorithm random forest uncertainty soft computing data science hydrometeorology LSTM rating curve method forecasting superpixel particle swarm optimization high-resolution remote-sensing images machine learning support vector machine Lower Yellow River extreme event management runoff series empirical wavelet transform Muskingum model hydrograph predictions bat algorithm data scarce basins Wilson flood self-organizing map big data extreme learning machine (ELM) hydroinformatics nonlinear Muskingum model invasive weed optimization rainfall–runoff flood forecasting artificial neural networks flash-flood streamflow predictions precipitation-runoff the upper Yangtze River survey parameters Haraz watershed ANN time series prediction postprocessing flood susceptibility modeling rainfall-runoff deep learning database LSTM network ensemble technique hybrid neural network self-organizing map (SOM) data assimilation particle filter algorithm monthly streamflow forecasting Dongting Lake machine learning methods micro-model stopping criteria Google Maps cultural algorithm wolf pack algorithm flood events urban water bodies Karahan flood St. Venant equations hybrid & hydrologic model |
| 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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Information Theory and Its Application in Machine Condition Monitoring
| Information Theory and Its Application in Machine Condition Monitoring |
| Autore | Li Yongbo |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (194 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
fault detection
deep learning transfer learning anomaly detection bearing wind turbines misalignment fault diagnosis information fusion improved artificial bee colony algorithm LSSVM D-S evidence theory optimal bandwidth kernel density estimation JS divergence domain adaptation partial transfer subdomain rotating machinery gearbox signal interception peak extraction cubic spline interpolation envelope combined fault diagnosis empirical wavelet transform grey wolf optimizer low pass FIR filter support vector machine satellite momentum wheel Huffman-multi-scale entropy (HMSE) support vector machine (SVM) adaptive particle swarm optimization (APSO) rail surface defect detection machine vision YOLOv4 MobileNetV3 multi-source heterogeneous fusion |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566460803321 |
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Li Yongbo
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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