{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:05:07Z","timestamp":1773414307717,"version":"3.50.1"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,15]]},"DOI":"10.1109\/geoinformatics57846.2022.9963824","type":"proceedings-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T21:05:43Z","timestamp":1670015143000},"page":"1-6","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of Air Quality in Nanjing Based on Recurrent Neural Network"],"prefix":"10.1109","author":[{"given":"Siqin","family":"Ban","sequence":"first","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benyi","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhai","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications,Nanjing,China,210023"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TNNLS.2020.2985720","article-title":"LSTM-MS Net: leveraging forecasts on sets of related timeseries with multiple seasonal patterns","volume":"32","author":"k","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems 487 Journal of Beijing University of Technology"},{"key":"ref11","first-page":"790","article-title":"Action recognition with hierarchical convolutional neural networks features and bi-directional long short-term memory model","volume":"34","author":"ge","year":"2017","journal-title":"Control Theory & Applications"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/S0048-9697(01)00991-3"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2010.12.040"},{"key":"ref14","first-page":"67","article-title":"PM_(2.5) prediction based on LSTM recurrent neural network","volume":"36","author":"bai","year":"2019","journal-title":"Computer Applications and Software"},{"key":"ref15","first-page":"193","article-title":"Prediction model of PM_(2.5) concentration based on KNN-LSTM","volume":"29","author":"song","year":"2020","journal-title":"Computer System and Applications"},{"key":"ref16","first-page":"480","article-title":"Time Series Prediction Method Based on Simplified LSTM Neural Network","volume":"47","author":"li","year":"2021","journal-title":"Journal of Beijing University of Technology"},{"key":"ref17","first-page":"57","article-title":"Air quality Index prediction based on improved LSTM network","volume":"37","author":"shi","year":"2021","journal-title":"Statistics and Decision"},{"key":"ref18","first-page":"1","article-title":"Comparison and Analysis of PM_(2.5) Prediction effect based on Tree Model in Beijing","volume":"39","author":"li","year":"2021","journal-title":"Environmental Engineering"},{"key":"ref19","article-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","author":"ke","year":"0","journal-title":"Annual Conference on Neural Information Processing Systems"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-017-5086-2"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2017.11.054"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.082"},{"key":"ref7","first-page":"662","article-title":"Research of stock price prediction based on DMD - LSTM model","volume":"3","author":"shi","year":"2020","journal-title":"Journal of Application Research of Computers"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural network","volume":"2","author":"d","year":"1991","journal-title":"IEEE Transactions on Neural Networks"},{"key":"ref9","first-page":"1521","article-title":"Short-term traffic flow prediction based on KNN-LSTM","volume":"44","author":"luo","year":"2018","journal-title":"Journal of Beijing University of Technology"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.11.053"},{"key":"ref20","first-page":"21","article-title":"A new combined forecasting method based on the characteristics of load forecasting model","volume":"25","author":"zhao","year":"2008","journal-title":"Modern Electric Power"},{"key":"ref22","first-page":"373","article-title":"Extreme value analysis of air quality index","volume":"35","author":"wang","year":"2019","journal-title":"Journal of Harbin University of Commerce (natural Science edition)"},{"key":"ref21","first-page":"23","article-title":"Research on new Short-term Load Forecasting Model based on XGBoost Algorithm","volume":"56","author":"chen","year":"2019","journal-title":"Electrical Measurement & Instrumentation"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.inffus.2018.07.007","article-title":"Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach","volume":"49","author":"f","year":"2019","journal-title":"Information Fusion"},{"key":"ref23","article-title":"LSTM Short-term power load Forecasting Based on seasonal Index and Adam Optimization","author":"ma","year":"2020","journal-title":"Beijing University of Technology"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.113381"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.029"}],"event":{"name":"2022 29th International Conference on Geoinformatics","location":"Beijing, China","start":{"date-parts":[[2022,8,15]]},"end":{"date-parts":[[2022,8,18]]}},"container-title":["2022 29th International Conference on Geoinformatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9963788\/9963792\/09963824.pdf?arnumber=9963824","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T19:56:11Z","timestamp":1671479771000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9963824\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,15]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/geoinformatics57846.2022.9963824","relation":{},"subject":[],"published":{"date-parts":[[2022,8,15]]}}}