Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management.
I tiakina i:
| Hōputu: | Online |
|---|---|
| Reo: | Ingarihi |
| I whakaputaina: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| Ngā marau: | |
| Urunga tuihono: | ONIX_20220111_9783036517209_410 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
| _version_ | 1869528653727006720 |
|---|---|
| collection | Directory of Open Access Books |
| description | The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management. |
| format | Online |
| id | doab-20.500.12854ir-76675 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-766752024-03-27T16:34:43Z Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management Kisi, Ozgur 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management. 2022-01-11T13:38:46Z 2022-01-11T13:38:46Z 2021 book ONIX_20220111_9783036517209_410 9783036517209 9783036517193 https://directory.doabooks.org/handle/20.500.12854/76675 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4122 https://mdpi.com/books/pdfview/book/4122 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-1719-3 10.3390/books978-3-0365-1719-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036517209 9783036517193 238 Basel, Switzerland open access |
| spellingShingle | 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
| title | Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
| title_full | Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
| title_fullStr | Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
| title_full_unstemmed | Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
| title_short | Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
| title_sort | machine learning with metaheuristic algorithms for sustainable water resources management |
| topic | 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20220111_9783036517209_410 |