{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T10:06:19Z","timestamp":1726135579200},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030869786"},{"type":"electronic","value":"9783030869793"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86979-3_44","type":"book-chapter","created":{"date-parts":[[2021,9,11]],"date-time":"2021-09-11T14:02:18Z","timestamp":1631368938000},"page":"629-640","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimation of Hourly Salinity Concentrations Using an Artificial Neural Network"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6629-5639","authenticated-orcid":false,"given":"Vladimir J.","family":"Alarcon","sequence":"first","affiliation":[]},{"given":"Anna C.","family":"Linhoss","sequence":"additional","affiliation":[]},{"given":"Christopher R.","family":"Kelble","sequence":"additional","affiliation":[]},{"given":"Paul F.","family":"Mickle","sequence":"additional","affiliation":[]},{"given":"Joseph","family":"Bishop","sequence":"additional","affiliation":[]},{"given":"Emily","family":"Milton","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,12]]},"reference":[{"key":"44_CR1","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1175\/BAMS-D-15-00171.1","volume":"98","author":"PA Conrads","year":"2017","unstructured":"Conrads, P.A., Darby, L.S.: Development of a coastal drought index using salinity data. Bull. Am. Meteor. Soc. 98, 753\u2013766 (2017). https:\/\/doi.org\/10.1175\/BAMS-D-15-00171.1","journal-title":"Bull. Am. Meteor. Soc."},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Conrads, P.A., Roehl Jr., E.A.: Analysis of salinity intrusion in the Waccamaw River and the Atlantic Intracoastal Waterway near Myrtle Beach, South Carolina, 1995\u20132002. USGS Scientific Investigations Rep. 2007\u20135110, 41 pp (2007)","DOI":"10.3133\/sir20075110"},{"key":"44_CR3","unstructured":"Shaw, J E., Zamorano, M.: Saltwater Interface Monitoring and Mapping Program. Water Resources Division, South Florida Water Management District (2020). https:\/\/www.sfwmd.gov\/sites\/default\/files\/documents\/ws-58_swi_mapping_report_final.pdf"},{"key":"44_CR4","doi-asserted-by":"publisher","unstructured":"Abiy, A.Z., Melesse, A.M., Abtew, W., Whitman, D.: Rainfall trend and variability in Southeast Florida: Implications for freshwater availability in the Everglades. PLoS ONE 14(2), e0212008 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0212008","DOI":"10.1371\/journal.pone.0212008"},{"key":"44_CR5","doi-asserted-by":"publisher","unstructured":"Stalker, J., Price, R., Swart, P.: Determining spatial and temporal inputs of freshwater, including submarine groundwater discharge, to a subtropical estuary using geochemical tracers, Biscayne Bay. South Florida. Estuaries and Coasts 32, 694\u2013708 (2009). https:\/\/doi.org\/10.1007\/s12237-009-9155-y","DOI":"10.1007\/s12237-009-9155-y"},{"key":"44_CR6","doi-asserted-by":"publisher","unstructured":"Lorenz, J.J.: A review of the effects of altered hydrology and salinity on vertebrate fauna and their habitats in Northeastern Florida Bay. Wetlands 34(1), 189\u2013200 (2013). https:\/\/doi.org\/10.1007\/s13157-013-0377-1","DOI":"10.1007\/s13157-013-0377-1"},{"key":"44_CR7","volume-title":"Tidally forced saltwater intrusion into the Coral Gables Canal","author":"VJ Alarcon","year":"2021","unstructured":"Alarcon, V.J., Linhoss, A., Kelble, C., Sanchez, G., Mardonez, F., et al.: Tidally forced saltwater intrusion into the Coral Gables Canal. Florida, USA (2021). (In process)"},{"key":"44_CR8","doi-asserted-by":"publisher","first-page":"363","DOI":"10.3390\/su13010363","volume":"13","author":"VJ Alarcon","year":"2021","unstructured":"Alarcon, V.J.: Hindcasting and forecasting total suspended sediment concentrations using a NARX neural network. Sustainability 13, 363 (2021). https:\/\/doi.org\/10.3390\/su13010363","journal-title":"Sustainability"},{"key":"44_CR9","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.jhydrol.2016.07.048","volume":"541","author":"HA Afan","year":"2016","unstructured":"Afan, H.A., El-Shafie, A., Mohtar, W.H.M.W., Yaseen, Z.M.: Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction. J. Hydrol. 541, 902\u2013913 (2016). https:\/\/doi.org\/10.1016\/j.jhydrol.2016.07.048","journal-title":"J. Hydrol."},{"key":"44_CR10","doi-asserted-by":"publisher","first-page":"75","DOI":"10.5004\/dwt.2021.26709","volume":"213","author":"ES Shahid","year":"2021","unstructured":"Shahid, E.S., Salari, M., Rastegar, M., Sheibani, S.N., Ehteshami, M.: Artificial neural network and mathematical approach for estimation of surface water quality parameters (Case study: California, USA). Desalin. Water Treat. 213, 75\u201383 (2021). https:\/\/doi.org\/10.5004\/dwt.2021.26709","journal-title":"Desalin. Water Treat."},{"issue":"6","key":"44_CR11","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.2166\/hydro.2019.073","volume":"21","author":"K Lin","year":"2021","unstructured":"Lin, K., Lu, P., Xu, C.-Y., Yu, X., Lan, T., Chen, X.: Modeling saltwater intrusion using an integrated Bayesian model averaging method in the Pearl River Delta. J. Hydroinf. 21(6), 1147\u20131162 (2021). https:\/\/doi.org\/10.2166\/hydro.2019.073","journal-title":"J. Hydroinf."},{"key":"44_CR12","doi-asserted-by":"publisher","unstructured":"Zhou, F., Liu, B., Duan, K.: Coupling wavelet transform and artificial neural network for forecasting estuarine salinity. J. Hydrol. 588, art. no. 125127 (2020). https:\/\/doi.org\/10.1016\/j.jhydrol.2020.125127","DOI":"10.1016\/j.jhydrol.2020.125127"},{"key":"44_CR13","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.envsoft.2017.03.022","volume":"93","author":"JS Rath","year":"2017","unstructured":"Rath, J.S., Hutton, P.H., Chen, L., Roy, S.B.: A hybrid empirical-Bayesian artificial neural network model of salinity in the San Francisco Bay-Delta estuary. Environ. Model. Softw. 93, 193\u2013208 (2017). https:\/\/doi.org\/10.1016\/j.envsoft.2017.03.022","journal-title":"Environ. Model. Softw."},{"key":"44_CR14","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1016\/j.marpolbul.2005.08.002","volume":"50","author":"V Caccia","year":"2005","unstructured":"Caccia, V., Boyer, J.: Spatial patterning of water quality in Biscayne Bay, Florida as a function of land use and water management. Mar. Pollut. Bull. 50, 1416\u20131429 (2005). https:\/\/doi.org\/10.1016\/j.marpolbul.2005.08.002","journal-title":"Mar. Pollut. Bull."},{"key":"44_CR15","doi-asserted-by":"publisher","unstructured":"USGS.: Changing Salinity Patterns in Biscayne Bay, Florida. Prepared in cooperation with South Florida Water Management District and Biscayne National Park (2004). https:\/\/doi.org\/10.3133\/fs20043108","DOI":"10.3133\/fs20043108"},{"key":"44_CR16","doi-asserted-by":"publisher","first-page":"620","DOI":"10.3390\/en11030620","volume":"11","author":"Z Boussaada","year":"2018","unstructured":"Boussaada, Z., Curea, O., Remaci, A., Camblong, H., Mrabet, N.B.: A Nonlinear Autoregressive Exogenous (NARX) Neural network model for the prediction of the daily direct solar radiation. Energies 11, 620 (2018). https:\/\/doi.org\/10.3390\/en11030620","journal-title":"Energies"},{"key":"44_CR17","doi-asserted-by":"publisher","unstructured":"Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885\u2013900 (2007). https:\/\/doi.org\/10.13031\/2013.23153","DOI":"10.13031\/2013.23153"},{"key":"44_CR18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.wsj.2017.12.002","volume":"32","author":"R Ang","year":"2018","unstructured":"Ang, R., Oeurng, C.: Simulating streamflow in an ungauged catchment of Tonlesap Lake Basin in Cambodia using Soil and Water Assessment Tool (SWAT) model. Water Sci. 32, 89\u2013101 (2018). https:\/\/doi.org\/10.1016\/j.wsj.2017.12.002","journal-title":"Water Sci."},{"key":"44_CR19","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.5194\/hess-23-4323-2019","volume":"23","author":"WJM Knoben","year":"2019","unstructured":"Knoben, W.J.M., Freer, J.E., Woods, R.A.: Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrol. Earth Syst. Sci. 23, 4323\u20134331 (2019). https:\/\/doi.org\/10.5194\/hess-23-4323-2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"44_CR20","doi-asserted-by":"publisher","unstructured":"Hussein, A.A.: Derivation and comparison of open-loop and closed-loop neural network battery state-of-charge estimators. Energy Procedia 75, 1856\u20131861 (2015). https:\/\/doi.org\/10.1016\/j.egypro.2015.07.163","DOI":"10.1016\/j.egypro.2015.07.163"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86979-3_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T04:09:14Z","timestamp":1638590954000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86979-3_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030869786","9783030869793"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86979-3_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"12 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Customed version of CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1588","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"466","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}