LEADER 04545nam 2201453z- 450 001 9910346688303321 005 20231214133707.0 035 $a(CKB)4920000000094786 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/47751 035 $a(EXLCZ)994920000000094786 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFlood Forecasting Using Machine Learning Methods 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 electronic resource (376 p.) 311 $a3-03897-548-6 330 $aThis book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water 610 $anatural hazards & 610 $aartificial neural network 610 $aflood routing 610 $athe Three Gorges Dam 610 $abacktracking search optimization algorithm (BSA) 610 $alag analysis 610 $aartificial intelligence 610 $aclassification and regression trees (CART) 610 $adecision tree 610 $areal-time 610 $aoptimization 610 $aensemble empirical mode decomposition (EEMD) 610 $aimproved bat algorithm 610 $aconvolutional neural networks 610 $aANFIS 610 $amethod of tracking energy differences (MTED) 610 $aadaptive neuro-fuzzy inference system (ANFIS) 610 $arecurrent nonlinear autoregressive with exogenous inputs (RNARX) 610 $adisasters 610 $aflood prediction 610 $aANN-based models 610 $aflood inundation map 610 $aensemble machine learning 610 $aflood forecast 610 $asensitivity 610 $ahydrologic models 610 $aphase space reconstruction 610 $awater level forecast 610 $adata forward prediction 610 $aearly flood warning systems 610 $abees algorithm 610 $arandom forest 610 $auncertainty 610 $asoft computing 610 $adata science 610 $ahydrometeorology 610 $aLSTM 610 $arating curve method 610 $aforecasting 610 $asuperpixel 610 $aparticle swarm optimization 610 $ahigh-resolution remote-sensing images 610 $amachine learning 610 $asupport vector machine 610 $aLower Yellow River 610 $aextreme event management 610 $arunoff series 610 $aempirical wavelet transform 610 $aMuskingum model 610 $ahydrograph predictions 610 $abat algorithm 610 $adata scarce basins 610 $aWilson flood 610 $aself-organizing map 610 $abig data 610 $aextreme learning machine (ELM) 610 $ahydroinformatics 610 $anonlinear Muskingum model 610 $ainvasive weed optimization 610 $arainfall?runoff 610 $aflood forecasting 610 $aartificial neural networks 610 $aflash-flood 610 $astreamflow predictions 610 $aprecipitation-runoff 610 $athe upper Yangtze River 610 $asurvey 610 $aparameters 610 $aHaraz watershed 610 $aANN 610 $atime series prediction 610 $apostprocessing 610 $aflood susceptibility modeling 610 $arainfall-runoff 610 $adeep learning 610 $adatabase 610 $aLSTM network 610 $aensemble technique 610 $ahybrid neural network 610 $aself-organizing map (SOM) 610 $adata assimilation 610 $aparticle filter algorithm 610 $amonthly streamflow forecasting 610 $aDongting Lake 610 $amachine learning methods 610 $amicro-model 610 $astopping criteria 610 $aGoogle Maps 610 $acultural algorithm 610 $awolf pack algorithm 610 $aflood events 610 $aurban water bodies 610 $aKarahan flood 610 $aSt. Venant equations 610 $ahybrid & 610 $ahydrologic model 700 $aChang$b Fi-John$4auth$01287662 702 $aHsu$b Kuolin$4auth 702 $aChang$b Li-Chiu$4auth 906 $aBOOK 912 $a9910346688303321 996 $aFlood Forecasting Using Machine Learning Methods$93020270 997 $aUNINA