{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:34:44Z","timestamp":1743143684703,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031170973"},{"type":"electronic","value":"9783031170980"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-17098-0_7","type":"book-chapter","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T14:19:41Z","timestamp":1664288381000},"page":"125-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Decision Support System Based on Rainfall Nowcasting and Artificial Neural Networks to Mitigate Wastewater Treatment Plant Downstream Floods"],"prefix":"10.1007","author":[{"given":"Loris Francesco","family":"Termite","sequence":"first","affiliation":[]},{"given":"Emanuele","family":"Bonamente","sequence":"additional","affiliation":[]},{"given":"Alberto","family":"Garinei","sequence":"additional","affiliation":[]},{"given":"Daniele","family":"Bolpagni","sequence":"additional","affiliation":[]},{"given":"Lorenzo","family":"Menculini","sequence":"additional","affiliation":[]},{"given":"Marcello","family":"Marconi","sequence":"additional","affiliation":[]},{"given":"Lorenzo","family":"Biondi","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Chini","sequence":"additional","affiliation":[]},{"given":"Massimo","family":"Crespi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","first-page":"850","DOI":"10.3390\/w12030850","volume":"12","author":"U Dittmer","year":"2020","unstructured":"Dittmer, U., Bachmann-Machnik, A., Launay, M.A.: Impact of combined sewer systems on the quality of urban streams: frequency and duration of elevated micropollutant concentrations. Water 12, 850 (2020)","journal-title":"Water"},{"issue":"1","key":"7_CR2","doi-asserted-by":"publisher","first-page":"173","DOI":"10.2166\/wst.2019.264","volume":"80","author":"A Pereira","year":"2019","unstructured":"Pereira, A., Pinho, J.L.S., Vieira, J.M.P., Faria, R., Costa, C.: Improving operational management of wastewater systems. A case study. Water Sci. Technol. 80(1), 173\u2013183 (2019)","journal-title":"Water Sci. Technol."},{"key":"7_CR3","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.autcon.2012.11.017","volume":"30","author":"T Park","year":"2013","unstructured":"Park, T., Kim, H.A.: A data warehouse-based decision support system for sewer infrastructure management. Autom. Constr. 30, 37\u201349 (2013)","journal-title":"Autom. Constr."},{"issue":"3","key":"7_CR4","first-page":"134","volume":"10","author":"M Rao","year":"2015","unstructured":"Rao, M.: A performance measurement application for a wastewater treatment plant. Int. J. Serv. Stan. 10(3), 134\u2013147 (2015)","journal-title":"Int. J. Serv. Stan."},{"key":"7_CR5","first-page":"77","volume":"113","author":"S Rechdaoui-Gu\u00e9rin","year":"2018","unstructured":"Rechdaoui-Gu\u00e9rin, S., et al.: Monitoring the quality of effluents in a unitary sanitation network. Tech.-Sci.-Methodes 113, 77\u201390 (2018)","journal-title":"Tech.-Sci.-Methodes"},{"key":"7_CR6","unstructured":"US EPA.: Smart data infrastructure for wet weather control and decision support. EPA 830-B-17\u2013004 (2018)"},{"issue":"15","key":"7_CR7","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1080\/10643389.2020.1757957","volume":"51","author":"A Botturi","year":"2020","unstructured":"Botturi, A., et al.: Combined sewer overflows: a critical review on best practice and innovative solutions to mitigate impacts on environment and human health. Crit. Rev. Environ. Sci. Technol. 51(15), 1585\u20131618 (2020)","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.proeng.2014.11.237","volume":"89","author":"M Carbone","year":"2014","unstructured":"Carbone, M., Garofalo, G., Piro, P.: Decentralized real time control in combined sewer system by using smart objects. Procedia Eng. 89, 473\u2013478 (2014)","journal-title":"Procedia Eng."},{"issue":"1","key":"7_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1061\/(ASCE)EE.1943-7870.0001013","volume":"142","author":"A Campisano","year":"2016","unstructured":"Campisano, A., Creaco, E., Modica, C.: Application of real-time control techniques to reduce water volume discharges from quality-oriented CSO devices. J. Environ. Eng. 142(1), 1\u20138 (2016)","journal-title":"J. Environ. Eng."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Termite, L.F., et al.: An artificial neural network-based real time DSS to manage the discharges of a wastewater treatment plant and reduce the flooding risk. In: Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021) SCITEPRESS \u2013 Science and Technology Publications, pp. 15\u201326 (2021)","DOI":"10.5220\/0010396500150026"},{"issue":"8","key":"7_CR11","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1016\/j.envsoft.2010.02.003","volume":"25","author":"HR Maier","year":"2010","unstructured":"Maier, H.R., Jain, A., Dandy, G.C., Sudheer, K.P.: Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ. Model. Softw. 25(8), 891\u2013909 (2010)","journal-title":"Environ. Model. Softw."},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"3427","DOI":"10.5194\/hess-21-3427-2017","volume":"21","author":"MP Clark","year":"2017","unstructured":"Clark, M.P., et al.: The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism. Hydrol. Earth Syst. Sci. 21, 3427\u20133440 (2017)","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"8","key":"7_CR13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"10","key":"7_CR14","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1175\/1520-0477(1998)079<2079:NTASR>2.0.CO;2","volume":"79","author":"JW Wilson","year":"1998","unstructured":"Wilson, J.W., Crook, N.A., Mueller, C.K., Sun, J., Dixon, M.: Nowcasting thunderstorms: a status report. Bull. Am. Meteor. Soc. 79(10), 2079\u20132100 (1998)","journal-title":"Bull. Am. Meteor. Soc."},{"issue":"661","key":"7_CR15","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1002\/qj.878","volume":"137","author":"L Panziera","year":"2011","unstructured":"Panziera, L., Germann, U., Gabella, M., Mandapaka, P.V.: NORA \u2013Nowcasting of orographic rainfall by means of analogues. Quart. J. Roy. Meteorol. Soc. 137(661), 2106\u20132123 (2011)","journal-title":"Quart. J. Roy. Meteorol. Soc."},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Bellon, A., Zawadzki, I., Kilambi, A., Lee, H.C., Lee, Y.H., Lee, G.: McGill algorithm for precipitation nocasting by Lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: sensitiity studies of the variational echo tracking (VET) technique. Asia-Pac. J. Atmos. Sci. 46(3), 369\u2013381 (2010)","DOI":"10.1007\/s13143-010-1008-x"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Lee, H.C., et al.: McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part II: real-time verification for the summer season. Asia-Pac. J. Atmos. Sci. 46(3), 383\u2013391 (2010)","DOI":"10.1007\/s13143-010-1009-9"},{"key":"7_CR18","unstructured":"Gregori, V., De Tomasi, F., Ferrari, G. Chini, A.: A comparison of nowcasting methods on the Italian radar mosaic. 2nd level master degree thesis, University of Salento. http:\/\/master.meteorologiaeoceanografiafisica.unisalento.it\/images\/students\/1920_vgregori\/tesi_vgregori_en.pdf, Accessed 04\/08\/2021"},{"issue":"1","key":"7_CR19","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1175\/WAF-D-11-00050.1","volume":"27","author":"PV Mandapaka","year":"2012","unstructured":"Mandapaka, P.V., Germann, U., Panziera, L., Hering, A.: Can Lagrangian extrapolation of radar fields be used for precipitation nowcasting over complex Alpine orography? Weather Forecast. 27(1), 28\u201349 (2012)","journal-title":"Weather Forecast."},{"issue":"11","key":"7_CR20","doi-asserted-by":"publisher","first-page":"7845","DOI":"10.1109\/TGRS.2020.2984594","volume":"58","author":"S Pulkkinen","year":"2020","unstructured":"Pulkkinen, S., Chandrasekar, V., von Lerber, A., Harri, A.M.: Nowcasting of convective rainfall using volumetric radar observations. IEEE Trans. Geosci. Remote Sens. 58(11), 7845\u20137859 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"7_CR21","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1175\/JHM481.1","volume":"7","author":"B Boudevillain","year":"2006","unstructured":"Boudevillain, B., Andrieu, H., Chaumerliac, N.: Evaluation of RadVil, a radar-based very short-term rainfall forecasting model. J. Hydrometeorol. 7(1), 178\u2013189 (2006)","journal-title":"J. Hydrometeorol."},{"issue":"3","key":"7_CR22","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1175\/1520-0450(2003)042<0381:ADASSA>2.0.CO;2","volume":"42","author":"AW Seed","year":"2003","unstructured":"Seed, A.W.: A dynamic and spatial scaling approach to advection forecasting. J. Appl. Meteorol. 42(3), 381\u2013388 (2003)","journal-title":"J. Appl. Meteorol."},{"key":"7_CR23","unstructured":"Lucas, B. D. and Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial intelligence, vol. 2, pp. 674\u2013679 (1981)"},{"issue":"2","key":"7_CR24","doi-asserted-by":"publisher","first-page":"59","DOI":"10.21014\/acta_imeko.v9i2.797","volume":"9","author":"A Marini","year":"2020","unstructured":"Marini, A., Termite, L.F., Garinei, A., Marconi, M., Biondi, L.: Neural network models for soil moisture forecasting from remotely sensed measurements. Acta Imeko 9(2), 59\u201365 (2020)","journal-title":"Acta Imeko"},{"key":"7_CR25","unstructured":"Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (2015)"},{"issue":"15","key":"7_CR26","doi-asserted-by":"publisher","first-page":"4505","DOI":"10.1007\/s11269-010-9670-4","volume":"24","author":"J Jeong","year":"2010","unstructured":"Jeong, J., Kannan, N., Arnold, J., Glick, R., Gosselink, L., Srinivasan, R.: Development and integration of sub-hourly rainfall\u2013runoff modeling capability within a watershed model. Water Resour. Manage 24(15), 4505\u20134527 (2010)","journal-title":"Water Resour. Manage"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Proietti, M., et al.: Edge Intelligence with Deep Learning in Greenhouse Management. In: Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021), pp. 180\u2013187. SCITEPRESS \u2013 Science and Technology Publications (2021)","DOI":"10.5220\/0010451701800187"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Menculini, L., et al.: Comparing prophet and deep learning to ARIMA in forecasting wholesale food prices. arXiv:2107.12770 (2021)","DOI":"10.3390\/forecast3030040"},{"key":"7_CR29","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.scs.2018.01.053","volume":"38","author":"BN Silva","year":"2018","unstructured":"Silva, B.N., Khan, M., Han, K.: Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 38, 697\u2013713 (2018)","journal-title":"Sustain. Cities Soc."}],"container-title":["Communications in Computer and Information Science","Smart Cities, Green Technologies, and Intelligent Transport Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17098-0_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T14:23:44Z","timestamp":1664288624000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17098-0_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031170973","9783031170980"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17098-0_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SMARTGREENS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Smart Cities and Green ICT Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icscg2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/smartgreens.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","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":"9","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":"0","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":"28% - 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":"3","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}