{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:55:14Z","timestamp":1742932514587,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030788100"},{"type":"electronic","value":"9783030788117"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78811-7_46","type":"book-chapter","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:22:37Z","timestamp":1625613757000},"page":"491-499","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Improved Spatial-Temporal Network Based on Residual Correction and\u00a0Evolutionary Algorithm for Water Quality Prediction"],"prefix":"10.1007","author":[{"given":"Xin","family":"Yu","sequence":"first","affiliation":[]},{"given":"Wenqiang","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Dongfan","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Qingjian","family":"Ni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"46_CR1","doi-asserted-by":"publisher","first-page":"103978103978","DOI":"10.1016\/j.chemolab.2020.103978","volume":"200","author":"T Rajaee","year":"2020","unstructured":"Rajaee, T., Khani, S., Ravansalar, M.: Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: a review. Chemometr. Intell. Lab. Syst. 200, 103978103978 (2020)","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Qin, Y., Song, D., Cheng, H., Cheng, W., Jiang, G., Cottrell, G.W.: A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 2627\u20132633 (2017)","DOI":"10.24963\/ijcai.2017\/366"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Liang, Y., Ke, S., Zhang, J., Yi, X., Zheng, Y.: GeoMAN: multi-level attention networks for geo-sensory time series prediction. In: IJCAI, pp. 3428\u20133434 (2018)","DOI":"10.24963\/ijcai.2018\/476"},{"issue":"8","key":"46_CR4","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"S-Y Shih","year":"2019","unstructured":"Shih, S.-Y., Sun, F.-K., Lee, H.: Temporal pattern attention for multivariate time series forecasting. Mach. Learn. 108(8), 1421\u20131441 (2019). https:\/\/doi.org\/10.1007\/s10994-019-05815-0","journal-title":"Mach. Learn."},{"issue":"7","key":"46_CR5","first-page":"1929","volume":"12","author":"J Yan","year":"2020","unstructured":"Yan, J., Gao, Y., Yu, Y., Xu, H., Xu, Z.: A prediction model based on deep belief network and least squares SVR applied to cross-section water quality. Water (Switzerland) 12(7), 1929 (2020)","journal-title":"Water (Switzerland)"},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Peng, W., Ni, Q.: A hybrid SVM-LSTM temperature prediction model based on empirical mode decomposition and residual prediction. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1616\u20131621. IEEE (2020)","DOI":"10.1109\/SMC42975.2020.9282824"},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"Yao, H., Liu, Y., Wei, Y., Tang, X., Li, Z.: Learning from multiple cities: a meta-learning approach for spatial-temporal prediction. In: The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp. 2181\u20132191 (2019)","DOI":"10.1145\/3308558.3313577"},{"key":"46_CR8","doi-asserted-by":"publisher","first-page":"124084","DOI":"10.1016\/j.jhydrol.2019.124084","volume":"578","author":"AN Ahmed","year":"2019","unstructured":"Ahmed, A.N., et al.: Machine learning methods for better water quality prediction. J. Hydrol. 578, 124084 (2019)","journal-title":"J. Hydrol."},{"key":"46_CR9","doi-asserted-by":"publisher","first-page":"125220","DOI":"10.1016\/j.jhydrol.2020.125220","volume":"590","author":"N Noori","year":"2020","unstructured":"Noori, N., Kalin, L., Isik, S.: Water quality prediction using SWAT-ANN coupled approach. J. Hydrol. 590, 125220 (2020)","journal-title":"J. Hydrol."},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Shi, Q., et al.: Block Hankel tensor ARIMA for multiple short time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5758\u20135766 (2020)","DOI":"10.1609\/aaai.v34i04.6032"},{"issue":"3","key":"46_CR11","doi-asserted-by":"publisher","first-page":"143","DOI":"10.2991\/ijndc.2017.5.3.3","volume":"5","author":"J Guo","year":"2017","unstructured":"Guo, J., Sato, Y.: A pair-wise bare bones particle swarm optimization algorithm for nonlinear functions. Int. J. Networked Distrib. Comput. 5(3), 143\u2013151 (2017)","journal-title":"Int. J. Networked Distrib. Comput."}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78811-7_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T23:29:10Z","timestamp":1625614150000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78811-7_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030788100","9783030788117"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78811-7_46","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":"7 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"17 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iasei.org\/icsi2021\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","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":"104","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":"59% - 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":"4-5","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)"}}]}}