03765nam 2200613 a 450 991078670590332120230803025937.01-61499-246-0(CKB)2670000000356091(EBL)1183056(OCoLC)841486700(SSID)ssj0001425603(PQKBManifestationID)11829307(PQKBTitleCode)TC0001425603(PQKBWorkID)11382524(PQKB)10315023(MiAaPQ)EBC1183056(Au-PeEL)EBL1183056(CaPaEBR)ebr10697705(CaONFJC)MIL494411(EXLCZ)99267000000035609120130522d2013 uy 0engur|n|---|||||txtccrThe role of bathymetry, wave obliquity and coastal curvature in dune erosion prediction[electronic resource] /Cornelis Den HeijerAmsterdam IOS Press BV20131 online resource (192 p.)Deltares select series,1877-5608 ;v. 12Description based upon print version of record.1-61499-245-2 Includes bibliographical references.Title Page; Abstract; Samenvatting; Contents; Introduction; Background; Dune erosion; Problem definition; Aim; Approach; Review of safety assessment components; Safety assessment; Strategy; Implementation; Inventory of relevant factors controlling dune erosion; Dune erosion models; DUROS/DUROS+/D++; DUROSTA; XBEACH; Applicability of models along the Dutch coast; Probabilistic methods; Monte Carlo; First Order Reliability Method (FORM); Bayesian Network; Limitations of dune erosion models; Primary variables; Cross-shore bathymetry; JARKUS profiles; Discussion; Wave obliquity; DUROSTA; XBEACHDiscussionConceptual wave obliquity model; Coastal curvature; DUROSTA; XBEACH; Discussion; Conceptual coastal curvature model; Probabilistic analysis; Present approach with FORM; Model setup; Resulting probability of exceedance; Resulting sensitivity; XBEACH; Conclusion; Present approach with Bayesian Network; Model setup; Prediction skill; Log-likelihood ratio; Results; Conclusions; Extended approach with wave obliquity and coastal curvature; FORM; Bayesian Network; Discussion; FORM vs. Bayesian Network approach; Generic value of the results; Case study of Dutch dune coastReference approachMethods; Results; Discussion; Conclusions; Extended approach with wave obliquity and coastal curvature; Methods; Results; Discussion; Conclusions and recommendations; General; Conclusions; Recommendations; References; Appendix A. XBEACH model settings; Acronyms; List of Figures; List of Tables; Acknowledgements; Curriculum Vitae"Low lying coastal areas have always been attractive for people to live, but are also prone to flooding. In The Netherlands, half of the population lives in the coastal area below mean sea level where two-thirds of the economic value is located. Coastal dunes protect the hinterland from floodings as a primary sea defence along the major part of the Dutch coastline."--Abstract.Deltares select series ;v. 12.Beach erosionNetherlandsSand dunesNetherlandsCoast changesNetherlandsBeach erosionSand dunesCoast changes551.302Den Heijer Cornelis1560595MiAaPQMiAaPQMiAaPQBOOK9910786705903321The role of bathymetry, wave obliquity and coastal curvature in dune erosion prediction3826698UNINA03543nam 22005775 450 991033780370332120251116203224.03-319-97436-X10.1007/978-3-319-97436-1(CKB)4100000005679056(MiAaPQ)EBC5491918(DE-He213)978-3-319-97436-1(PPN)22991750X(EXLCZ)99410000000567905620180811d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Business /by Rajendra Akerkar1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (92 pages)SpringerBriefs in Business,2191-54823-319-97435-1 Chapter 1: Introduction to Artificial Intelligence -- Chapter 2: Machine Learning -- Chapter 3: Deep Learning -- Chapter 4: Recommendation Engines -- Chapter 5: Natural Language Processing -- Chapter 6: Employing AI in Business.This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes.SpringerBriefs in Business,2191-5482Information technologyBusiness—Data processingArtificial intelligenceManagementIndustrial managementIT in Businesshttps://scigraph.springernature.com/ontologies/product-market-codes/522000Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Innovation/Technology Managementhttps://scigraph.springernature.com/ontologies/product-market-codes/518000Information technology.Business—Data processing.Artificial intelligence.Management.Industrial management.IT in Business.Artificial Intelligence.Innovation/Technology Management.006.3Akerkar Rajendraauthttp://id.loc.gov/vocabulary/relators/aut621740BOOK9910337803703321Artificial Intelligence for Business2026654UNINA