00989nam a2200241 i 4500991002132349707536070213s2006 it 000 0 ita d8831181912b13480674-39ule_instDip.to Filologia Class. e Scienze FilosoficheitaFirmicus Maternus, Iulius185791L'errore delle religioni pagane/Firmico Materno ; introduzione, traduzione e note a cura di Ennio SanziRoma :Città Nuova,2006201 p. ; 20 cmCollana di testi patristici ;191Contiene riferimenti bibliografici. IndiciSanzi, Ennio.b1348067402-04-1413-02-07991002132349707536LE007 Sala A Testi Patr. Firmicus 0112007000115190le007pE18.00-l- 01010.i1436676913-02-07Errore delle religioni pagane1095923UNISALENTOle00713-02-07ma -itait 2005544nam 2201189z- 450 991055711070332120210501(CKB)5400000000040939(oapen)https://directory.doabooks.org/handle/20.500.12854/68957(oapen)doab68957(EXLCZ)99540000000004093920202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvances in Hydrologic Forecasts and Water Resources ManagementBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online 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 and information: generalbicsscartificial intelligenceartificial neural networkscascade hydropower reservoirscascade reservoirschanging environmentsclimate change impactscoupled modelsdammed lakedata synthesisdata-scarce deglaciating river basindegree of balance and approachelastic-ball modificationelasticity coefficientempirical mode decompositionfeasible search spaceflood controlflood riskflood-risk mapforecast evaluationgeneralized likelihood uncertainty estimationGeneralized Likelihood Uncertainty Estimation (GLUE)GloFAS-SeasonalGR4J modelgravitational search algorithmgrey entropy methodhighly urbanized areaHushan reservoirhydrodynamic modellinghydrologic forecastingimpoundment operationInternet of Things (IoT)interval numberlandslideloss-benefit ratio of ecology and power generationmachine learningmachine learning modelMahalanobis-Taguchi Systemmulti-objective optimal operation modelmulti-objective optimizationmulti-objective reservoir operationNDVIopposition learningparameter uncertaintyPareto-front optimal solution setpartial mutationprobabilistic forecastrandom forestrecurrent nonlinear autoregressive with exogenous inputs (RNARX)regional flood inundation depthriskSequential Gaussian Simulationsignal-to-noise ratiosmall and medium-scale riversSnowmelt Runoff ModelTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS)temporal transferabilityThree Gorges Reservoirtime-varying parameterTOPSISuncertaintyuncertainty analysisUnscented Kalman Filterurban hydrological modelurban stormwaterwater resources managementwestern Chinawhole region perspectiveYangtze RiverYarlung Zangbo RiverResearch and information: generalChang Fi-Johnedt1287662Guo ShenglianedtChang Fi-JohnothGuo ShenglianothBOOK9910557110703321Advances in Hydrologic Forecasts and Water Resources Management3037442UNINA