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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 electronic resource (272 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
water resources management
landslide dammed lake flood risk time-varying parameter GR4J model changing environments temporal transferability western China cascade hydropower reservoirs multi-objective optimization TOPSIS gravitational search algorithm opposition learning partial mutation elastic-ball modification Snowmelt Runoff Model parameter uncertainty data-scarce deglaciating river basin climate change impacts generalized likelihood uncertainty estimation Yangtze River cascade reservoirs impoundment operation GloFAS-Seasonal forecast evaluation small and medium-scale rivers highly urbanized area flood control whole region perspective coupled models flood-risk map hydrodynamic modelling Sequential Gaussian Simulation urban stormwater probabilistic forecast Unscented Kalman Filter artificial neural networks Three Gorges Reservoir Mahalanobis-Taguchi System grey entropy method signal-to-noise ratio degree of balance and approach interval number multi-objective optimal operation model feasible search space Pareto-front optimal solution set loss–benefit ratio of ecology and power generation elasticity coefficient empirical mode decomposition Hushan reservoir data synthesis urban hydrological model Generalized Likelihood Uncertainty Estimation (GLUE) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) uncertainty analysis NDVI Yarlung Zangbo River machine learning model random forest Internet of Things (IoT) regional flood inundation depth recurrent nonlinear autoregressive with exogenous inputs (RNARX) artificial intelligence machine learning multi-objective reservoir operation hydrologic forecasting uncertainty risk |
| 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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