TY - BOOK ID - 145107510 TI - Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Research & information: general KW - groundwater KW - artificial intelligence KW - hydrologic model KW - groundwater level prediction KW - machine learning KW - principal component analysis KW - spatiotemporal variation KW - uncertainty analysis KW - hydroinformatics KW - support vector machine KW - big data KW - artificial neural network KW - nitrogen compound KW - nitrogen prediction KW - prediction models KW - neural network KW - non-linear modeling KW - PACF KW - WANN KW - SVM-LF KW - SVM-RF KW - Govindpur KW - streamflow forecasting KW - Bayesian model averaging KW - multivariate adaptive regression spline KW - M5 model tree KW - Kernel extreme learning machines KW - South Korea KW - uncertainty KW - sustainability KW - prediction intervals KW - ungauged basin KW - streamflow simulation KW - satellite precipitation KW - atmospheric reanalysis KW - ensemble modeling KW - additive regression KW - bagging KW - dagging KW - random subspace KW - rotation forest KW - flood routing KW - Muskingum method KW - extension principle KW - calibration KW - fuzzy sets and systems KW - particle swarm optimization KW - EEFlux KW - irrigation performance KW - CWP KW - water conservation KW - NDVI KW - water resources KW - Daymet V3 KW - Google Earth Engine KW - improved extreme learning machine (IELM) KW - sensitivity analysis KW - shortwave radiation flux density KW - sustainable development UR - https://www.unicat.be/uniCat?func=search&query=sysid:145107510 AB - 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. ER -