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 | ||
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Lo trovi qui: Univ. Federico II | ||
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Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
Autore | Kisi Ozgur |
Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
Descrizione fisica | 1 electronic resource (238 p.) |
Soggetto topico | Research & information: general |
Soggetto non controllato |
groundwater
artificial intelligence hydrologic model groundwater level prediction machine learning principal component analysis spatiotemporal variation uncertainty analysis hydroinformatics support vector machine big data artificial neural network nitrogen compound nitrogen prediction prediction models neural network non-linear modeling PACF WANN SVM-LF SVM-RF Govindpur streamflow forecasting Bayesian model averaging multivariate adaptive regression spline M5 model tree Kernel extreme learning machines South Korea uncertainty sustainability prediction intervals ungauged basin streamflow simulation satellite precipitation atmospheric reanalysis ensemble modeling additive regression bagging dagging random subspace rotation forest flood routing Muskingum method extension principle calibration fuzzy sets and systems particle swarm optimization EEFlux irrigation performance CWP water conservation NDVI water resources Daymet V3 Google Earth Engine improved extreme learning machine (IELM) sensitivity analysis shortwave radiation flux density sustainable development |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910557448103321 |
Kisi Ozgur
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
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