{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:39:53Z","timestamp":1774669193840,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819757596","type":"print"},{"value":"9789819757602","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5760-2_11","type":"book-chapter","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:36:51Z","timestamp":1724049411000},"page":"153-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reservoir Flood Prediction Service Based on\u00a0Seq2seq Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9804-6042","authenticated-orcid":false,"given":"Lincong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4108-1391","authenticated-orcid":false,"given":"Shijun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6157-3740","authenticated-orcid":false,"given":"Li","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"issue":"1","key":"11_CR1","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s12652-020-02761-x","volume":"13","author":"H Abbasimehr","year":"2022","unstructured":"Abbasimehr, H., Paki, R.: Improving time series forecasting using LSTM and attention models. J. Ambient Intell. Humaniz. Comput. 13(1), 673\u2013691 (2022)","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"11_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Chen, S.M., Hwang, J.R.: Temperature prediction using fuzzy time series. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 30(2), 263\u2013275 (2000)","DOI":"10.1109\/3477.836375"},{"key":"11_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2020.125734","volume":"594","author":"Z Fang","year":"2021","unstructured":"Fang, Z., Wang, Y., Peng, L., Hong, H.: Predicting flood susceptibility using LSTM neural networks. J. Hydrol. 594, 125734 (2021)","journal-title":"J. Hydrol."},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1007\/s00521-017-3210-6","volume":"31","author":"A Farzad","year":"2019","unstructured":"Farzad, A., Mashayekhi, H., Hassanpour, H.: A comparative performance analysis of different activation functions in LSTM networks for classification. Neural Comput. Appl. 31, 2507\u20132521 (2019)","journal-title":"Neural Comput. Appl."},{"key":"11_CR6","first-page":"1","volume":"2022","author":"TO Hodson","year":"2022","unstructured":"Hodson, T.O.: Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not. Geosci. Model Dev. Discuss. 2022, 1\u201310 (2022)","journal-title":"Geosci. Model Dev. Discuss."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Kong, X., Li, Z., Liu, Z., et\u00a0al.: Flood prediction in ungauged basins by physical-based topkapi model. Adv. Meteorolo. 2019 (2019)","DOI":"10.1155\/2019\/4795853"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Koprinska, I., Wu, D., Wang, Z.: Convolutional neural networks for energy time series forecasting. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489399"},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.advwatres.2019.05.020","volume":"130","author":"ZW Kundzewicz","year":"2019","unstructured":"Kundzewicz, Z.W., Su, B., Wang, Y., Xia, J., Huang, J., Jiang, T.: Flood risk and its reduction in China. Adv. Water Resour. 130, 37\u201345 (2019)","journal-title":"Adv. Water Resour."},{"issue":"1","key":"11_CR10","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3390\/w13010030","volume":"13","author":"J Lee","year":"2020","unstructured":"Lee, J., Lee, J.E., Kim, N.W.: Estimation of hourly flood hydrograph from daily flows using artificial neural network and flow disaggregation technique. Water 13(1), 30 (2020)","journal-title":"Water"},{"key":"11_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113082","volume":"143","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Gong, C., Yang, L., Chen, Y.: DSTP-RNN: a dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction. Expert Syst. Appl. 143, 113082 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"11_CR12","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.marpolbul.2003.10.004","volume":"48","author":"T McClanahan","year":"2004","unstructured":"McClanahan, T., Sala, E., Mumby, P., Jones, S.: Phosphorus and nitrogen enrichment do not enhance brown frondose\u201d macroalgae\u201d. Mar. Pollut. Bull. 48(1), 196\u2013199 (2004)","journal-title":"Mar. Pollut. Bull."},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Mehrmolaei, S., Keyvanpour, M.R.: Time series forecasting using improved Arima. In: 2016 Artificial Intelligence and Robotics (IRANOPEN), pp. 92\u201397. IEEE (2016)","DOI":"10.1109\/RIOS.2016.7529496"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Mishra, N., Soni, H.K., Sharma, S., Upadhyay, A.: A comprehensive survey of data mining techniques on time series data for rainfall prediction. J. ICT Res. Appl. 11(2) (2017)","DOI":"10.5614\/itbj.ict.res.appl.2017.11.2.4"},{"issue":"11","key":"11_CR15","doi-asserted-by":"publisher","first-page":"4776","DOI":"10.3390\/su12114776","volume":"12","author":"PF Orr\u00f9","year":"2020","unstructured":"Orr\u00f9, P.F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R., Arena, S.: Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry. Sustainability 12(11), 4776 (2020)","journal-title":"Sustainability"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Pollock, D.S.G., Green, R.C., Nguyen, T.: Handbook of Time Series Analysis, Signal Processing, and Dynamics. Elsevier, Amsterdam (1999)","DOI":"10.1016\/B978-012560990-6\/50003-8"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"4123","DOI":"10.1007\/s11269-019-02345-1","volume":"33","author":"Y Qi","year":"2019","unstructured":"Qi, Y., Zhou, Z., Yang, L., Quan, Y., Miao, Q.: A decomposition-ensemble learning model based on LSTM neural network for daily reservoir inflow forecasting. Water Resour. Manag. 33, 4123\u20134139 (2019)","journal-title":"Water Resour. Manag."},{"issue":"14","key":"11_CR18","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1007\/s12517-022-10564-x","volume":"15","author":"M Skariah","year":"2022","unstructured":"Skariah, M., Suriyakala, C.D.: Forecasting reservoir inflow combining exponential smoothing, Arima, and LSTM models. Arab. J. Geosci. 15(14), 1292 (2022)","journal-title":"Arab. J. Geosci."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Tian, Y., Pan, L.: Predicting short-term traffic flow by long short-term memory recurrent neural network. In: 2015 IEEE international conference on smart city\/SocialCom\/SustainCom (SmartCity), pp. 153\u2013158. IEEE (2015)","DOI":"10.1109\/SmartCity.2015.63"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.jhydrol.2012.11.017","volume":"476","author":"M Valipour","year":"2013","unstructured":"Valipour, M., Banihabib, M.E., Behbahani, S.M.R.: Comparison of the Arma, Arima, and the autoregressive artificial neural network models in forecasting the monthly inflow of DEZ dam reservoir. J. Hydrol. 476, 433\u2013441 (2013)","journal-title":"J. Hydrol."},{"key":"11_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123483","volume":"247","author":"CE Velasquez","year":"2022","unstructured":"Velasquez, C.E., Zocatelli, M., Estanislau, F.B., Castro, V.F.: Analysis of time series models for Brazilian electricity demand forecasting. Energy 247, 123483 (2022)","journal-title":"Energy"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Vuong, P.H., Dat, T.T., Mai, T.K., Uyen, P.H., et\u00a0al.: Stock-price forecasting based on Boost and LSTM. Comput. Syst. Scie. Eng. 40(1) (2022)","DOI":"10.32604\/csse.2022.017685"},{"issue":"13","key":"11_CR23","doi-asserted-by":"publisher","first-page":"9621","DOI":"10.1007\/s00521-019-04474-5","volume":"32","author":"W Waheeb","year":"2020","unstructured":"Waheeb, W., Ghazali, R.: A novel error-output recurrent neural network model for time series forecasting. Neural Comput. Appl. 32(13), 9621\u20139647 (2020)","journal-title":"Neural Comput. Appl."},{"key":"11_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107094","volume":"224","author":"Y Xiao","year":"2021","unstructured":"Xiao, Y., Li, Y., Yuan, J., Guo, S., Xiao, Y., Li, Z.: History-based attention in seq2seq model for multi-label text classification. Knowl.-Based Syst. 224, 107094 (2021)","journal-title":"Knowl.-Based Syst."},{"issue":"8","key":"11_CR25","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.3390\/w13081031","volume":"13","author":"J Xu","year":"2021","unstructured":"Xu, J., Wang, K., Lin, C., Xiao, L., Huang, X., Zhang, Y.: FM-GRU: a time series prediction method for water quality based on seq2seq framework. Water 13(8), 1031 (2021)","journal-title":"Water"},{"issue":"8","key":"11_CR26","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.3390\/app9081665","volume":"9","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Li, D., Wang, Y., Fang, Y., Xiao, W.: Abstract text summarization with a convolutional seq2seq model. Appl. Sci. 9(8), 1665 (2019)","journal-title":"Appl. Sci."},{"issue":"24","key":"11_CR27","doi-asserted-by":"publisher","first-page":"7211","DOI":"10.3390\/s20247211","volume":"20","author":"K Zhou","year":"2020","unstructured":"Zhou, K., Wang, W., Hu, T., Deng, K.: Time series forecasting and classification models based on recurrent with attention mechanism and generative adversarial networks. Sensors 20(24), 7211 (2020)","journal-title":"Sensors"}],"container-title":["Communications in Computer and Information Science","Service Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5760-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:06:25Z","timestamp":1724306785000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5760-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819757596","9789819757602"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5760-2_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Service Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icss22024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccf.org.cn\/ICSS2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}