LEADER 05525nam 2201177z- 450 001 9910557110703321 005 20231214132848.0 035 $a(CKB)5400000000040939 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68957 035 $a(EXLCZ)995400000000040939 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Hydrologic Forecasts and Water Resources Management 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (272 p.) 311 $a3-03936-804-4 311 $a3-03936-805-2 330 $aThe impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management. 606 $aResearch & information: general$2bicssc 610 $awater resources management 610 $alandslide 610 $adammed lake 610 $aflood risk 610 $atime-varying parameter 610 $aGR4J model 610 $achanging environments 610 $atemporal transferability 610 $awestern China 610 $acascade hydropower reservoirs 610 $amulti-objective optimization 610 $aTOPSIS 610 $agravitational search algorithm 610 $aopposition learning 610 $apartial mutation 610 $aelastic-ball modification 610 $aSnowmelt Runoff Model 610 $aparameter uncertainty 610 $adata-scarce deglaciating river basin 610 $aclimate change impacts 610 $ageneralized likelihood uncertainty estimation 610 $aYangtze River 610 $acascade reservoirs 610 $aimpoundment operation 610 $aGloFAS-Seasonal 610 $aforecast evaluation 610 $asmall and medium-scale rivers 610 $ahighly urbanized area 610 $aflood control 610 $awhole region perspective 610 $acoupled models 610 $aflood-risk map 610 $ahydrodynamic modelling 610 $aSequential Gaussian Simulation 610 $aurban stormwater 610 $aprobabilistic forecast 610 $aUnscented Kalman Filter 610 $aartificial neural networks 610 $aThree Gorges Reservoir 610 $aMahalanobis-Taguchi System 610 $agrey entropy method 610 $asignal-to-noise ratio 610 $adegree of balance and approach 610 $ainterval number 610 $amulti-objective optimal operation model 610 $afeasible search space 610 $aPareto-front optimal solution set 610 $aloss?benefit ratio of ecology and power generation 610 $aelasticity coefficient 610 $aempirical mode decomposition 610 $aHushan reservoir 610 $adata synthesis 610 $aurban hydrological model 610 $aGeneralized Likelihood Uncertainty Estimation (GLUE) 610 $aTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) 610 $auncertainty analysis 610 $aNDVI 610 $aYarlung Zangbo River 610 $amachine learning model 610 $arandom forest 610 $aInternet of Things (IoT) 610 $aregional flood inundation depth 610 $arecurrent nonlinear autoregressive with exogenous inputs (RNARX) 610 $aartificial intelligence 610 $amachine learning 610 $amulti-objective reservoir operation 610 $ahydrologic forecasting 610 $auncertainty 610 $arisk 615 7$aResearch & information: general 700 $aChang$b Fi-John$4edt$01287662 702 $aGuo$b Shenglian$4edt 702 $aChang$b Fi-John$4oth 702 $aGuo$b Shenglian$4oth 906 $aBOOK 912 $a9910557110703321 996 $aAdvances in Hydrologic Forecasts and Water Resources Management$93037442 997 $aUNINA