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.
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (238 p.) |
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| 100 | 1 | |a Kisi, Ozgur |4 edt | |
| 245 | 1 | 0 | |a Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (238 p.) | ||
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| 520 | |a 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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 653 | |a groundwater | ||
| 653 | |a artificial intelligence | ||
| 653 | |a hydrologic model | ||
| 653 | |a groundwater level prediction | ||
| 653 | |a machine learning | ||
| 653 | |a principal component analysis | ||
| 653 | |a spatiotemporal variation | ||
| 653 | |a uncertainty analysis | ||
| 653 | |a hydroinformatics | ||
| 653 | |a support vector machine | ||
| 653 | |a big data | ||
| 653 | |a artificial neural network | ||
| 653 | |a nitrogen compound | ||
| 653 | |a nitrogen prediction | ||
| 653 | |a prediction models | ||
| 653 | |a neural network | ||
| 653 | |a non-linear modeling | ||
| 653 | |a PACF | ||
| 653 | |a WANN | ||
| 653 | |a SVM-LF | ||
| 653 | |a SVM-RF | ||
| 653 | |a Govindpur | ||
| 653 | |a streamflow forecasting | ||
| 653 | |a Bayesian model averaging | ||
| 653 | |a multivariate adaptive regression spline | ||
| 653 | |a M5 model tree | ||
| 653 | |a Kernel extreme learning machines | ||
| 653 | |a South Korea | ||
| 653 | |a uncertainty | ||
| 653 | |a sustainability | ||
| 653 | |a prediction intervals | ||
| 653 | |a ungauged basin | ||
| 653 | |a streamflow simulation | ||
| 653 | |a satellite precipitation | ||
| 653 | |a atmospheric reanalysis | ||
| 653 | |a ensemble modeling | ||
| 653 | |a additive regression | ||
| 653 | |a bagging | ||
| 653 | |a dagging | ||
| 653 | |a random subspace | ||
| 653 | |a rotation forest | ||
| 653 | |a flood routing | ||
| 653 | |a Muskingum method | ||
| 653 | |a extension principle | ||
| 653 | |a calibration | ||
| 653 | |a fuzzy sets and systems | ||
| 653 | |a particle swarm optimization | ||
| 653 | |a EEFlux | ||
| 653 | |a irrigation performance | ||
| 653 | |a CWP | ||
| 653 | |a water conservation | ||
| 653 | |a NDVI | ||
| 653 | |a water resources | ||
| 653 | |a Daymet V3 | ||
| 653 | |a Google Earth Engine | ||
| 653 | |a improved extreme learning machine (IELM) | ||
| 653 | |a sensitivity analysis | ||
| 653 | |a shortwave radiation flux density | ||
| 653 | |a sustainable development | ||
| 776 | |z 3-0365-1720-0 | ||
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| 700 | 1 | |a Kisi, Ozgur |4 oth | |
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| ADM | |b 2023-12-15 05:40:05 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
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