Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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 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
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 electronic resource (248 p.) |
| Soggetto topico |
Research & information: general
Mathematics & science |
| Soggetto non controllato |
streamflow forecasting
C-vine copula quantile regression joint dependencies water resource management ecological relationship factorial analysis input-output analysis optimal path reduction urban solid waste system desalination reverse osmosis modelling simulation parameter estimation seawater boron watershed management nonpoint source pollution point source pollution water quality pollutant loadings South Texas eco-efficiency DEA CO2 emissions forecasting ecological indicators biomass gasification machine learning computer modeling computer simulation regression model reduction LASSO classification feature selection financial market investing sustainability renewable energy support energy modeling energy system design generation profile environmental footprint renewable energy electricity production unlisted companies Germany feed-in tariff biofuel policy investment profitability analysis the pay-off method simulation decomposition sourcing operational flexibility business aviation turboprop electric motor specific power Monte Carlo simulation Iowa food-energy-water nexus nitrogen export system modeling weather modeling optimal allocation interval fuzzy dynamic programming water resources |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557620403321 |
|
Yeomans Julian Scott
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||