{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:40:03Z","timestamp":1773330003606,"version":"3.50.1"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>As climate change increases the risk of extreme rainfall events, concerns over flood management have also increased. To recover quickly from flood damage and prevent further consequential damage, flood waste prediction is of utmost importance. Therefore, developing a rapid and accurate prediction of flood waste generation is important in order to reduce disaster. Several approaches of flood waste classification have been proposed by various researchers, however only a few focus on prediction of flood waste. In this study, a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) approach is adapted to address these challenges. Two different raw datasets were obtained from the \u201cAdvancing Sustainable Materials Management: Facts and Figures 2015\u201d source. The datasets were for 9\u00a0years (1960, 1970, 1980, 1990, 2000, 2005, 2010, 2014, 2015), and are labelled as the materials generated in the Municipal Waste Stream from 1960 to 2015 and the materials Recycled and Composted in Municipal Solid Waste from 1960 to 2015. The waste types were grouped as paper and paperboard (PP), glass (GI), metals (Mt), plastics (PI), rubber and leather (RL), textiles (Tt), wood (Wd), food (Fd), yard trimmings (YT) and miscellaneous inorganic wastes (IW).<\/jats:p>","DOI":"10.1007\/s44196-024-00485-w","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T11:02:46Z","timestamp":1713956566000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine"],"prefix":"10.1007","volume":"17","author":[{"given":"Farnaz","family":"Fatovatikhah","sequence":"first","affiliation":[]},{"given":"Ismail","family":"Ahmedy","sequence":"additional","affiliation":[]},{"given":"Rafidah Md","family":"Noor","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,24]]},"reference":[{"issue":"1","key":"485_CR1","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1080\/19942060.2018.1448896","volume":"12","author":"F Fotovatikhah","year":"2018","unstructured":"Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K., Faizollahzadeh Ardabili, S., Piran, M.D.J.: Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Eng. Appl. Comput. Fluid Mech. 12(1), 411\u2013437 (2018). https:\/\/doi.org\/10.1080\/19942060.2018.1448896","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"issue":"08","key":"485_CR2","doi-asserted-by":"publisher","first-page":"777","DOI":"10.9786\/kswm.2018.35.8.777","volume":"35","author":"MH Park","year":"2018","unstructured":"Park, M.H., Kim, H., Ju, M., Kim, H.J., Kim, J.Y.: Development of regional flood debris estimation model utilizing data of disaster annual report: case study on Ulsan city. J. Korea Soc. Waste Manag. 35(08), 777\u2013784 (2018). https:\/\/doi.org\/10.9786\/kswm.2018.35.8.777","journal-title":"J. Korea Soc. Waste Manag."},{"key":"485_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.118163","volume":"240","author":"H Wu","year":"2019","unstructured":"Wu, H., Zuo, J., Zillante, G., Wang, J., Yuan, H.: Status quo and future directions of construction and demolition waste research: A critical review. J. Clean. Prod. 240, 118163 (2019). https:\/\/doi.org\/10.1016\/j.jclepro.2019.118163","journal-title":"J. Clean. Prod."},{"issue":"12","key":"485_CR4","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1016\/j.wasman.2006.10.015","volume":"27","author":"J-R Chen","year":"2007","unstructured":"Chen, J.-R., Tsai, H.-Y., Hsu, P.-C., Shen, C.-C.: Estimation of waste generation from floods. Waste Manag. 27(12), 1717\u20131724 (2007). https:\/\/doi.org\/10.1016\/j.wasman.2006.10.015","journal-title":"Waste Manag."},{"key":"485_CR5","unstructured":"J. Brownlee, A gentle introduction to long short-term memory networks by the experts. In Machine Learning Mastery, May 23, 2017. https:\/\/machinelearningmastery.com\/gentle-introduction-long-short-term-memory-networks-experts\/ (accessed Dec. 08, 2021)."},{"issue":"7","key":"485_CR6","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.3390\/w11071387","volume":"11","author":"X-H Le","year":"2019","unstructured":"Le, X.-H., Ho, H.V., Lee, G., Jung, S.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7), 1387 (2019). https:\/\/doi.org\/10.3390\/w11071387","journal-title":"Water"},{"issue":"3","key":"485_CR7","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/j.jenvman.2009.10.007","volume":"91","author":"R Noori","year":"2010","unstructured":"Noori, R., Karbassi, A., Salman Sabahi, M.: Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. J. Environ. Manage. 91(3), 767\u2013771 (2010). https:\/\/doi.org\/10.1016\/j.jenvman.2009.10.007","journal-title":"J. Environ. Manage."},{"issue":"1","key":"485_CR8","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s11625-012-0161-9","volume":"8","author":"D Antanasijevi\u0107","year":"2013","unstructured":"Antanasijevi\u0107, D., Pocajt, V., Popovi\u0107, I., Red\u017ei\u0107, N., Risti\u0107, M.: The forecasting of municipal waste generation using artificial neural networks and sustainability indicators. Sustain. Sci. 8(1), 37\u201346 (2013). https:\/\/doi.org\/10.1007\/s11625-012-0161-9","journal-title":"Sustain. Sci."},{"key":"485_CR9","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.wasman.2016.05.018","volume":"56","author":"M Abbasi","year":"2016","unstructured":"Abbasi, M., El Hanandeh, A.: Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag. 56, 13\u201322 (2016). https:\/\/doi.org\/10.1016\/j.wasman.2016.05.018","journal-title":"Waste Manag."},{"key":"485_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.120387","volume":"256","author":"MdM Hoque","year":"2020","unstructured":"Hoque, Md.M., Rahman, M.T.U.: Landfill area estimation based on solid waste collection prediction using Ann model and final waste disposal options. J. Clean. Prod. 256, 120387 (2020). https:\/\/doi.org\/10.1016\/j.jclepro.2020.120387","journal-title":"J. Clean. Prod."},{"key":"485_CR11","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.wasman.2020.06.042","volume":"256","author":"MH Park","year":"2020","unstructured":"Park, M.H., Ju, M., Cho, Y.H., Kim, J.Y.: Data stratification toward advanced flood waste estimation: A case study in South Korea. Waste Manage. 256, 215\u2013224 (2020). https:\/\/doi.org\/10.1016\/j.wasman.2020.06.042","journal-title":"Waste Manage."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00485-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00485-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00485-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T11:09:54Z","timestamp":1713956994000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00485-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,24]]},"references-count":11,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["485"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00485-w","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,24]]},"assertion":[{"value":"2 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"103"}}