05525nam 2201177z- 450 991055711070332120231214132848.0(CKB)5400000000040939(oapen)https://directory.doabooks.org/handle/20.500.12854/68957(EXLCZ)99540000000004093920202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvances in Hydrologic Forecasts and Water Resources ManagementBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 electronic resource (272 p.)3-03936-804-4 3-03936-805-2 The 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.Research & information: generalbicsscwater resources managementlandslidedammed lakeflood risktime-varying parameterGR4J modelchanging environmentstemporal transferabilitywestern Chinacascade hydropower reservoirsmulti-objective optimizationTOPSISgravitational search algorithmopposition learningpartial mutationelastic-ball modificationSnowmelt Runoff Modelparameter uncertaintydata-scarce deglaciating river basinclimate change impactsgeneralized likelihood uncertainty estimationYangtze Rivercascade reservoirsimpoundment operationGloFAS-Seasonalforecast evaluationsmall and medium-scale rivershighly urbanized areaflood controlwhole region perspectivecoupled modelsflood-risk maphydrodynamic modellingSequential Gaussian Simulationurban stormwaterprobabilistic forecastUnscented Kalman Filterartificial neural networksThree Gorges ReservoirMahalanobis-Taguchi Systemgrey entropy methodsignal-to-noise ratiodegree of balance and approachinterval numbermulti-objective optimal operation modelfeasible search spacePareto-front optimal solution setloss–benefit ratio of ecology and power generationelasticity coefficientempirical mode decompositionHushan reservoirdata synthesisurban hydrological modelGeneralized Likelihood Uncertainty Estimation (GLUE)Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)uncertainty analysisNDVIYarlung Zangbo Rivermachine learning modelrandom forestInternet of Things (IoT)regional flood inundation depthrecurrent nonlinear autoregressive with exogenous inputs (RNARX)artificial intelligencemachine learningmulti-objective reservoir operationhydrologic forecastinguncertaintyriskResearch & information: generalChang Fi-Johnedt1287662Guo ShenglianedtChang Fi-JohnothGuo ShenglianothBOOK9910557110703321Advances in Hydrologic Forecasts and Water Resources Management3037442UNINA