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Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
| 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 online resource (238 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
additive regression
artificial intelligence artificial neural network atmospheric reanalysis bagging Bayesian model averaging big data calibration CWP dagging Daymet V3 EEFlux ensemble modeling extension principle flood routing fuzzy sets and systems Google Earth Engine Govindpur groundwater groundwater level prediction hydroinformatics hydrologic model improved extreme learning machine (IELM) irrigation performance Kernel extreme learning machines M5 model tree machine learning multivariate adaptive regression spline Muskingum method n/a NDVI neural network nitrogen compound nitrogen prediction non-linear modeling PACF particle swarm optimization prediction intervals prediction models principal component analysis random subspace rotation forest satellite precipitation sensitivity analysis shortwave radiation flux density South Korea spatiotemporal variation streamflow forecasting streamflow simulation support vector machine sustainability sustainable development SVM-LF SVM-RF uncertainty uncertainty analysis ungauged basin WANN water conservation water resources |
| 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 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics
| Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics |
| Autore | Yeomans Julian Scott |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (248 p.) |
| Soggetto topico |
Mathematics & science
Research & information: general |
| Soggetto non controllato |
biofuel policy
biomass gasification boron business aviation C-vine copula classification CO2 emissions computer modeling computer simulation DEA desalination dynamic programming eco-efficiency ecological indicators ecological relationship electric motor electricity production energy modeling energy system design environmental footprint factorial analysis feature selection feed-in tariff financial market forecasting fuzzy generation profile Germany input-output analysis interval investing investment profitability analysis Iowa food-energy-water nexus joint dependencies LASSO machine learning model reduction modelling Monte Carlo simulation n/a nitrogen export nonpoint source pollution operational flexibility optimal allocation optimal path parameter estimation point source pollution pollutant loadings quantile regression reduction regression renewable energy renewable energy support reverse osmosis seawater simulation simulation decomposition sourcing South Texas specific power streamflow forecasting sustainability system modeling the pay-off method turboprop unlisted companies urban solid waste system water quality water resource management water resources watershed management weather modeling |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557620403321 |
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Yeomans Julian Scott
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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