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Advances in Hydrologic Forecasts and Water Resources Management
| Advances in Hydrologic Forecasts and Water Resources Management |
| Autore | Chang Fi-John |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (272 p.) |
| Soggetto topico | Research and information: general |
| Soggetto non controllato |
artificial intelligence
artificial neural networks cascade hydropower reservoirs cascade reservoirs changing environments climate change impacts coupled models dammed lake data synthesis data-scarce deglaciating river basin degree of balance and approach elastic-ball modification elasticity coefficient empirical mode decomposition feasible search space flood control flood risk flood-risk map forecast evaluation generalized likelihood uncertainty estimation Generalized Likelihood Uncertainty Estimation (GLUE) GloFAS-Seasonal GR4J model gravitational search algorithm grey entropy method highly urbanized area Hushan reservoir hydrodynamic modelling hydrologic forecasting impoundment operation Internet of Things (IoT) interval number landslide loss-benefit ratio of ecology and power generation machine learning machine learning model Mahalanobis-Taguchi System multi-objective optimal operation model multi-objective optimization multi-objective reservoir operation NDVI opposition learning parameter uncertainty Pareto-front optimal solution set partial mutation probabilistic forecast random forest recurrent nonlinear autoregressive with exogenous inputs (RNARX) regional flood inundation depth risk Sequential Gaussian Simulation signal-to-noise ratio small and medium-scale rivers Snowmelt Runoff Model Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) temporal transferability Three Gorges Reservoir time-varying parameter TOPSIS uncertainty uncertainty analysis Unscented Kalman Filter urban hydrological model urban stormwater water resources management western China whole region perspective Yangtze River Yarlung Zangbo River |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557110703321 |
Chang Fi-John
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence Techniques in Hydrology and Water Resources Management / / Fi-John Chang, Li-Chiu Chang, Jui-Fa Chen, editors
| Artificial Intelligence Techniques in Hydrology and Water Resources Management / / Fi-John Chang, Li-Chiu Chang, Jui-Fa Chen, editors |
| Pubbl/distr/stampa | Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 |
| Descrizione fisica | 1 online resource (302 pages) |
| Disciplina | 551.48 |
| Soggetto topico | Hydrology |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910729782003321 |
| Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Flood Forecasting Using Machine Learning Methods / Li-Chiu Chang, Fi-John Chang, Kuolin Hsu
| Flood Forecasting Using Machine Learning Methods / Li-Chiu Chang, Fi-John Chang, Kuolin Hsu |
| Autore | Chang Li-Chiu |
| Pubbl/distr/stampa | Basel, Switzerland : , : MDPI, , 2019 |
| Descrizione fisica | 1 online resource (1 p.) |
| Soggetto non controllato | data science; big data; artificial intelligence; soft computing; extreme event management; time series prediction; LSTM; rainfall-runoff; flood events; flood forecasting; data assimilation; particle filter algorithm; micro-model; Lower Yellow River; ANN; hydrometeorology; flood forecasting; real-time; postprocessing; machine learning; early flood warning systems; hydroinformatics; database; flood forecast; Google Maps - data scarce basins; runoff series; data forward prediction; ensemble empirical mode decomposition (EEMD); stopping criteria; method of tracking energy differences (MTED); deep learning; convolutional neural networks; superpixel; urban water bodies; high-resolution remote-sensing images; monthly streamflow forecasting; artificial neural network; ensemble technique; phase space reconstruction; empirical wavelet transform; hybrid neural network; flood forecasting; self-organizing map; bat algorithm; particle swarm optimization; flood routing; Muskingum model; machine learning methods; St. Venant equations; rating curve method; nonlinear Muskingum model; hydrograph predictions; flood routing; Muskingum model; hydrologic models; improved bat algorithm; Wilson flood; Karahan flood; flood susceptibility modeling; ANFIS; cultural algorithm; bees algorithm; invasive weed optimization; Haraz watershed; ANN-based models; flood inundation map; self-organizing map (SOM); recurrent nonlinear autoregressive with exogenous inputs (RNARX); ensemble technique; artificial neural networks; uncertainty; streamflow predictions; sensitivity; flood forecasting; extreme learning machine (ELM); backtracking search optimization algorithm (BSA); the upper Yangtze River; deep learning; LSTM network; water level forecast; the Three Gorges Dam; Dongting Lake; Muskingum model; wolf pack algorithm; parameters; optimization; flood routing; flash-flood; precipitation-runoff; forecasting; lag analysis; random forest; machine learning; flood prediction; flood forecasting; hydrologic model; rainfall-runoff - hybrid & ensemble machine learning; artificial neural network; support vector machine; natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision tree; survey; classification and regression trees (CART) |
| ISBN |
9783038975496
3038975494 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910765788503321 |
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Chang Li-Chiu
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| Basel, Switzerland : , : MDPI, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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