{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T08:23:43Z","timestamp":1750753423471,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,13]]},"DOI":"10.1145\/3615892.3628481","type":"proceedings-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T19:45:45Z","timestamp":1701200745000},"page":"24-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A hybrid model for Forecasting Biological Oxygen Demand using CEEMDAN-LSTM"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1609-4564","authenticated-orcid":false,"given":"Neha","family":"Pant","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7960-4127","authenticated-orcid":false,"given":"Durga","family":"Toshniwal","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7224-1802","authenticated-orcid":false,"given":"Bhola Ram","family":"Gurjar","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Evaluation of modelled and measured patterns of dissolved oxygen in a freshwater lake as an indicator of the presence of biodegradable organic pollution. Water research 34, 4","author":"Ansa-Asare OD","year":"2000","unstructured":"OD Ansa-Asare , IL Marr , and MS Cresser . 2000. Evaluation of modelled and measured patterns of dissolved oxygen in a freshwater lake as an indicator of the presence of biodegradable organic pollution. Water research 34, 4 ( 2000 ), 1079--1088. OD Ansa-Asare, IL Marr, and MS Cresser. 2000. Evaluation of modelled and measured patterns of dissolved oxygen in a freshwater lake as an indicator of the presence of biodegradable organic pollution. Water research 34, 4 (2000), 1079--1088."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s00477-020-01776-2","article-title":"Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model","volume":"34","author":"Barzegar Rahim","year":"2020","unstructured":"Rahim Barzegar , Mohammad Taghi Aalami , and Jan Adamowski . 2020 . Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model . Stochastic Environmental Research and Risk Assessment 34 , 2 (2020), 415 -- 433 . Rahim Barzegar, Mohammad Taghi Aalami, and Jan Adamowski. 2020. Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model. Stochastic Environmental Research and Risk Assessment 34, 2 (2020), 415--433.","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1016\/j.advwatres.2005.10.007","article-title":"Stochastic modelling of DO and BOD components in a stream with random inputs","volume":"29","author":"Boano Fulvio","year":"2006","unstructured":"Fulvio Boano , Roberto Revelli , and Luca Ridolfi . 2006 . Stochastic modelling of DO and BOD components in a stream with random inputs . Advances in Water Resources 29 , 9 (2006), 1341 -- 1350 . Fulvio Boano, Roberto Revelli, and Luca Ridolfi. 2006. Stochastic modelling of DO and BOD components in a stream with random inputs. Advances in Water Resources 29, 9 (2006), 1341--1350.","journal-title":"Advances in Water Resources"},{"volume-title":"Nutrients and Eutrophication in Estuaries and Coastal Waters","author":"Garcia A","key":"e_1_3_2_1_4_1","unstructured":"A Garcia , JA Revilla , R Medina , C Alvarez , and JA Juanes . 2002. A model for predicting the temporal evolution of dissolved oxygen concentration in shallow estuaries . In Nutrients and Eutrophication in Estuaries and Coastal Waters . Springer , 205--211. A Garcia, JA Revilla, R Medina, C Alvarez, and JA Juanes. 2002. A model for predicting the temporal evolution of dissolved oxygen concentration in shallow estuaries. In Nutrients and Eutrophication in Estuaries and Coastal Waters. Springer, 205--211."},{"key":"e_1_3_2_1_5_1","unstructured":"Google. 2023. Google Maps API. https:\/\/www.google.com\/maps\/ Accessed 2023-09-13.  Google. 2023. Google Maps API. https:\/\/www.google.com\/maps\/ Accessed 2023-09-13."},{"key":"e_1_3_2_1_6_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_7_1","volume-title":"Wu. MC, Shih, HH, Zheng, Q., Yen, N.-C., Tung","author":"Huang Norden E","year":"1998","unstructured":"Norden E Huang and Z Shen . [n. d.]. Long. SR , Wu. MC, Shih, HH, Zheng, Q., Yen, N.-C., Tung , C. C., Liu, HH , 1998 . The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London , A 454 ([n. d.]), 903--995. Norden E Huang and Z Shen. [n. d.]. Long. SR, Wu. MC, Shih, HH, Zheng, Q., Yen, N.-C., Tung, C. C., Liu, HH, 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, A 454 ([n. d.]), 903--995."},{"key":"e_1_3_2_1_8_1","first-page":"3","article-title":"Modelling dissolved oxygen dynamics in coastal lagoons","volume":"211","author":"Hull Vincent","year":"2008","unstructured":"Vincent Hull , Luisa Parrella , and Margherita Falcucci . 2008 . Modelling dissolved oxygen dynamics in coastal lagoons . Ecological Modelling 211 , 3 -- 4 (2008), 468--480. Vincent Hull, Luisa Parrella, and Margherita Falcucci. 2008. Modelling dissolved oxygen dynamics in coastal lagoons. Ecological Modelling 211, 3--4 (2008), 468--480.","journal-title":"Ecological Modelling"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","first-page":"12875","DOI":"10.1007\/s11356-021-13875-w","article-title":"Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation","volume":"29","author":"Khullar Sakshi","year":"2022","unstructured":"Sakshi Khullar and Nanhey Singh . 2022 . Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation . Environmental Science and Pollution Research 29 , 9 (2022), 12875 -- 12889 . Sakshi Khullar and Nanhey Singh. 2022. Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation. Environmental Science and Pollution Research 29, 9 (2022), 12875--12889.","journal-title":"Environmental Science and Pollution Research"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","first-page":"126169","DOI":"10.1016\/j.chemosphere.2020.126169","article-title":"Hybrid decision tree-based machine learning models for short-term water quality prediction","volume":"249","author":"Lu Hongfang","year":"2020","unstructured":"Hongfang Lu and Xin Ma . 2020 . Hybrid decision tree-based machine learning models for short-term water quality prediction . Chemosphere 249 (2020), 126169 . Hongfang Lu and Xin Ma. 2020. Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249 (2020), 126169.","journal-title":"Chemosphere"},{"key":"e_1_3_2_1_11_1","volume-title":"Application and sensitivity analysis of artificial neural network for prediction of chemical oxygen demand. Water resources management 32, 1","author":"Ruben Gebdang B","year":"2018","unstructured":"Gebdang B Ruben , Ke Zhang , Hongjun Bao , and Xirong Ma. 2018. Application and sensitivity analysis of artificial neural network for prediction of chemical oxygen demand. Water resources management 32, 1 ( 2018 ), 273--283. Gebdang B Ruben, Ke Zhang, Hongjun Bao, and Xirong Ma. 2018. Application and sensitivity analysis of artificial neural network for prediction of chemical oxygen demand. Water resources management 32, 1 (2018), 273--283."},{"key":"e_1_3_2_1_12_1","volume-title":"Pattern Recognition: Statistical, Structural and Neural Approaches","author":"Schalkoff Robert","year":"1992","unstructured":"Robert Schalkoff . 1992 . Pattern Recognition: Statistical, Structural and Neural Approaches , John Wiley & Sons . Inc, New York (1992). Robert Schalkoff. 1992. Pattern Recognition: Statistical, Structural and Neural Approaches, John Wiley & Sons. Inc, New York (1992)."},{"key":"e_1_3_2_1_13_1","volume-title":"Artificial neural network modeling of the river water quality---a case study. Ecological modelling 220, 6","author":"Singh Kunwar P","year":"2009","unstructured":"Kunwar P Singh , Ankita Basant , Amrita Malik , and Gunja Jain . 2009. Artificial neural network modeling of the river water quality---a case study. Ecological modelling 220, 6 ( 2009 ), 888--895. Kunwar P Singh, Ankita Basant, Amrita Malik, and Gunja Jain. 2009. Artificial neural network modeling of the river water quality---a case study. Ecological modelling 220, 6 (2009), 888--895."},{"key":"e_1_3_2_1_14_1","volume-title":"Support vector machines in water quality management. Analytica chimica acta 703, 2","author":"Singh Kunwar P","year":"2011","unstructured":"Kunwar P Singh , Nikita Basant , and Shikha Gupta . 2011. Support vector machines in water quality management. Analytica chimica acta 703, 2 ( 2011 ), 152--162. Kunwar P Singh, Nikita Basant, and Shikha Gupta. 2011. Support vector machines in water quality management. Analytica chimica acta 703, 2 (2011), 152--162."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"e_1_3_2_1_16_1","volume-title":"Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis 1, 01","author":"Wu Zhaohua","year":"2009","unstructured":"Zhaohua Wu and Norden E Huang . 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis 1, 01 ( 2009 ), 1--41. Zhaohua Wu and Norden E Huang. 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis 1, 01 (2009), 1--41."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1007\/s11269-022-03224-y","article-title":"Study on water quality prediction of urban reservoir by coupled CEEMDAN decomposition and LSTM neural network model","volume":"36","author":"Zhang Lei","year":"2022","unstructured":"Lei Zhang , Zhiqiang Jiang , Shanshan He , Jiefeng Duan , Pengfei Wang , and Ting Zhou . 2022 . Study on water quality prediction of urban reservoir by coupled CEEMDAN decomposition and LSTM neural network model . Water Resources Management 36 , 10 (2022), 3715 -- 3735 . Lei Zhang, Zhiqiang Jiang, Shanshan He, Jiefeng Duan, Pengfei Wang, and Ting Zhou. 2022. Study on water quality prediction of urban reservoir by coupled CEEMDAN decomposition and LSTM neural network model. Water Resources Management 36, 10 (2022), 3715--3735.","journal-title":"Water Resources Management"}],"event":{"name":"GeoSocial '23: 1st ACM SIGSPATIAL International Workshop on Geocomputational Analysis of Socio-Economic Data","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"],"location":"Hamburg Germany","acronym":"GeoSocial '23"},"container-title":["Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geocomputational Analysis of Socio-Economic Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3615892.3628481","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3615892.3628481","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:28Z","timestamp":1750178188000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3615892.3628481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"references-count":17,"alternative-id":["10.1145\/3615892.3628481","10.1145\/3615892"],"URL":"https:\/\/doi.org\/10.1145\/3615892.3628481","relation":{},"subject":[],"published":{"date-parts":[[2023,11,13]]},"assertion":[{"value":"2023-11-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}