04342nam2 2200565 i 450 BVEE02095320170908093210.0uma- rai- e:is Aslu (3) 1582 (R)feiv. 320121128d1582 ||||0itac50 balatbez01i xxxe z01n˜\3!: P. Ouidii Nasonis œEpistolae Heroides, ab Hercule Ciofano Sulmonensi ope veterum librorum emendatæ, & obseruationibus illustratæAntuerpiaeex officina Christophori Plantini1582103, \1! p.8ºSeguono le Observationes su: Amores, Ars amatoria, Remedia amoris, Medicamina faciei, Nux, Halieutica (quest'ultima opera con proprio front. a c. f3)Marca sul frontCors. ; gr. ; romSegn.: a-f⁸g⁴.1 v.IT-NA0079, SALA FARN.39. C 291 v. - vol. 3 legato con i vol. 1,2,4 e con 4 carte autonome legate in fine: Hercules Ciofanus Iulio Agapito sulmonensis... - Rilegato male: posizionato dopo il vol. 4, precede una parte del. vol. 1 ([croce]8 2 [croce]8)IT-NA0079, SALA FARN.39. C 29v. [3] (legato con i v. [1-2; 4 e con 4 carte autonome legate in fine: Hercules Ciofanus Iulio Agapito sulmonensis ...)IT-NA0079, SALA FARN.39. C 29001BVEE0209452001 ˜Herculis Ciofani Sulmonensis œIn omnia P. Ouidii Nasonis opera obseruationes. Vna cum ipsius Ouidij vita, & descriptione SulmonisBEAnversaUBOL000154Ciofano, Ercole <m. 1592>CFIV129160070190300Ovidius Naso, PubliusCFIV002690Plantin, ChristopheBVEV017379650OvidAQ1V000257Ovidius Naso, PubliusOvidio Nasone, PublioCFIV002693Ovidius Naso, PubliusOvidiuCFIV078917Ovidius Naso, PubliusOvidioCFIV156621Ovidius Naso, PubliusOvidio Nasone, P.CFIV160367Ovidius Naso, PubliusOvidius, Publius NasoCFIV228966Ovidius Naso, PubliusObidiosCFIV230653Ovidius Naso, PubliusOvidio Naso, PubliusSBNV006544Ovidius Naso, PubliusNasone, Publio OvidioSBNV047182Ovidius Naso, PubliusOvideUFIV122075Ovidius Naso, PubliusCiofani, ErcoleMILV320009Ciofano, Ercole <m. 1592>Officina Christophori PlantiniCFIV287577Plantin, ChristophePlantinus, ChristophorusPUVV315936Plantin, ChristopheITIT-NA007920121128IT-NA0079Biblioteca Nazionale Vittorio Emanuele IIINA00793 esemplariShttp://books.google.com/books?vid=IBNN:BNA01001464564BVEE020953Biblioteca Nazionale Vittorio Emanuele III1 v. BNSALA FARN.39. C 29 BNA010014645645G B 1 v.C 2012112820121128vol. 1, 2, 3, 41 v. BNV.F. 152 B 70 BNVA10015451155 H (0003 1 v. - vol. 3 legato con i vol. 1,2,4 e con 4 carte autonome legate in fine: Hercules Ciofanus Iulio Agapito sulmonensis... - Rilegato male: posizionato dopo il vol. 4, precede una parte del. vol. 1 ([croce]8 2 [croce]8)C 2014110320141103Rilegato male: posizionato dopo il vol. 4, precede una parte del. vol. 1 ([croce]8 2 [croce]8)Imperfezioni4 v.v. 1-4 (legati con 4 carte autonome legate in fine: Hercules Ciofanus Iulio Agapito sulmonensis ...) BNV.F. 117 E 42.1 BNVA10015644875 B (0003 v. [3] (legato con i v. [1-2; 4 e con 4 carte autonome legate in fine: Hercules Ciofanus Iulio Agapito sulmonensis ...)C 2016051820160518legato con i v. [1-2; 4 e con 4 carte autonome legate in fine: Hercules Ciofanus Iulio Agapito sulmonensis ...Caratteristiche materialiBiblioteca Nazionale Vittorio Emanuele III1 BNA01001464564http://books.google.com/books?vid=IBNN:BNA01001464564 BNSALA FARN.39. C 29 BNEpistolae Heroides, ab Hercule Ciofano Sulmonensi ope veterum librorum emendatæ, & obseruationibus illustratæ1480435UNISANNIO04889nam 22003733a 450 991076578850332120250203235425.09783038975496303897549410.3390/books978-3-03897-549-6(CKB)5400000000000002(ScCtBLL)f963680f-b041-491e-902d-8ee3181380d7(OCoLC)1105805416(EXLCZ)99540000000000000220250203i20192019 uu enguru||||||||||txtrdacontentcrdamediacrrdacarrierFlood Forecasting Using Machine Learning MethodsLi-Chiu Chang, Fi-John Chang, Kuolin HsuBasel, Switzerland :MDPI,2019.1 online resource (1 p.)Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.data science; big data; artificial intelligence; soft computing; extreme event management; time series prediction; LSTM; rainfall-runoff; flood events; flood forecasting; data assimilation; particle filter algorithm; micro-model; Lower Yellow River; ANN; hydrometeorology; flood forecasting; real-time; postprocessing; machine learning; early flood warning systems; hydroinformatics; database; flood forecast; Google Mapsdata scarce basins; runoff series; data forward prediction; ensemble empirical mode decomposition (EEMD); stopping criteria; method of tracking energy differences (MTED); deep learning; convolutional neural networks; superpixel; urban water bodies; high-resolution remote-sensing images; monthly streamflow forecasting; artificial neural network; ensemble technique; phase space reconstruction; empirical wavelet transform; hybrid neural network; flood forecasting; self-organizing map; bat algorithm; particle swarm optimization; flood routing; Muskingum model; machine learning methods; St. Venant equations; rating curve method; nonlinear Muskingum model; hydrograph predictions; flood routing; Muskingum model; hydrologic models; improved bat algorithm; Wilson flood; Karahan flood; flood susceptibility modeling; ANFIS; cultural algorithm; bees algorithm; invasive weed optimization; Haraz watershed; ANN-based models; flood inundation map; self-organizing map (SOM); recurrent nonlinear autoregressive with exogenous inputs (RNARX); ensemble technique; artificial neural networks; uncertainty; streamflow predictions; sensitivity; flood forecasting; extreme learning machine (ELM); backtracking search optimization algorithm (BSA); the upper Yangtze River; deep learning; LSTM network; water level forecast; the Three Gorges Dam; Dongting Lake; Muskingum model; wolf pack algorithm; parameters; optimization; flood routing; flash-flood; precipitation-runoff; forecasting; lag analysis; random forest; machine learning; flood prediction; flood forecasting; hydrologic model; rainfall-runoffhybrid &amp; ensemble machine learning; artificial neural network; support vector machine; natural hazards &amp; disasters; adaptive neuro-fuzzy inference system (ANFIS); decision tree; survey; classification and regression trees (CART)Chang Li-Chiu1787621Chang Fi-JohnHsu KuolinScCtBLLScCtBLLBOOK9910765788503321Flood Forecasting Using Machine Learning Methods4321168UNINA