{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:15Z","timestamp":1750220895633,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,5]],"date-time":"2019-11-05T00:00:00Z","timestamp":1572912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["71673292,71673294"],"award-info":[{"award-number":["71673292,71673294"]}]},{"name":"National Key Research & Development (R&D) Plan under Grant","award":["2018YFC0806900"],"award-info":[{"award-number":["2018YFC0806900"]}]},{"name":"National Social Science Foundation of China under Grant","award":["17CGL047"],"award-info":[{"award-number":["17CGL047"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,11,5]]},"DOI":"10.1145\/3356998.3365768","type":"proceedings-article","created":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T16:08:52Z","timestamp":1596125332000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Strategy of hybrid optimization algorithms for source parameters estimation of hazardous gas in field cases"],"prefix":"10.1145","author":[{"given":"Yiduo","family":"Wang","sequence":"first","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"given":"Zhengqiu","family":"Zhu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"given":"Chuan","family":"AI","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]}],"member":"320","published-online":{"date-parts":[[2020,7,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1080\/15287399309531816"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1021\/es2017158"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0004-6981(83)90164-6"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1995)034<1320:BTLSDM>2.0.CO;2"},{"key":"e_1_3_2_1_5_1","volume-title":"Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere. Boundary-Layer Meteorology, 78(1--2), 191--210","author":"Wilson J. D.","year":"1996","unstructured":"J. D. Wilson and B. L. Sawford ( 1996 ). Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere. Boundary-Layer Meteorology, 78(1--2), 191--210 . J. D. Wilson and B. L. Sawford (1996). Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere. Boundary-Layer Meteorology, 78(1--2), 191--210."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2009.06.064"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2013.03.066"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/0957-1272(93)90007-S"},{"key":"e_1_3_2_1_9_1","volume-title":"Atmospheric dispersion modeling using artificial neural network based cellular automata. Environmental Modelling & Software, 85(56--69","author":"Lauret P.","year":"2016","unstructured":"P. Lauret , F. Heymes , L. Aprin and A. Johannet ( 2016 ). Atmospheric dispersion modeling using artificial neural network based cellular automata. Environmental Modelling & Software, 85(56--69 . P. Lauret, F. Heymes, L. Aprin and A. Johannet (2016). Atmospheric dispersion modeling using artificial neural network based cellular automata. Environmental Modelling & Software, 85(56--69."},{"key":"e_1_3_2_1_10_1","volume-title":"Probabilistic Inverse Theory. Advances in Geophysics, 52(1) - 102","author":"Debski W.","year":"2010","unstructured":"W. Debski ( 2010 ). Probabilistic Inverse Theory. Advances in Geophysics, 52(1) - 102 . W. Debski (2010). Probabilistic Inverse Theory. Advances in Geophysics, 52(1) - 102."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2018.01.050"},{"key":"e_1_3_2_1_12_1","volume-title":"A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Information Fusion, 36(S156625351630152X","author":"Hutchinson M.","year":"2016","unstructured":"M. Hutchinson , H. Oh and W. H. Chen ( 2016 ). A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Information Fusion, 36(S156625351630152X . M. Hutchinson, H. Oh and W. H. Chen (2016). A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors. Information Fusion, 36(S156625351630152X."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2005.08.027"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2006.10.003"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2004.07.015"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"S. Qiu B. Chen R. Wang Z. Zhu Y. Wang and X. Qiu (2018). Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network particle swarm optimization and expectation maximization. Atmospheric Environment 178(158--163.  S. Qiu B. Chen R. Wang Z. Zhu Y. Wang and X. Qiu (2018). Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network particle swarm optimization and expectation maximization. Atmospheric Environment 178(158--163.","DOI":"10.1016\/j.atmosenv.2018.01.056"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of ICNN'95-International Conference on Neural Networks","author":"Kennedy J.","year":"2002","unstructured":"J. Kennedy and R. Eberhart ( 2002 ). Particle swarm optimization . Proceedings of ICNN'95-International Conference on Neural Networks , 1942--1948 J. Kennedy and R. Eberhart (2002). Particle swarm optimization. Proceedings of ICNN'95-International Conference on Neural Networks, 1942--1948"},{"key":"e_1_3_2_1_18_1","volume-title":"Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere. Atmospheric Environment, 94(637--646","author":"Ma D.","year":"2014","unstructured":"D. Ma , S. Wang and Z. Zhang ( 2014 ). Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere. Atmospheric Environment, 94(637--646 . D. Ma, S. Wang and Z. Zhang (2014). Hybrid algorithm of minimum relative entropy-particle swarm optimization with adjustment parameters for gas source term identification in atmosphere. Atmospheric Environment, 94(637--646."},{"volume-title":"1958 Project prairie grass, a field program in diffusion","author":"Barad M. L.","key":"e_1_3_2_1_19_1","unstructured":"M. L. Barad , 1958 Project prairie grass, a field program in diffusion . Volume 1 , AIR FORCE CAMBRIDGE RESEARCH LABS HANSCOM AFB MA. M. L. Barad, 1958 Project prairie grass, a field program in diffusion. Volume 1, AIR FORCE CAMBRIDGE RESEARCH LABS HANSCOM AFB MA."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2010.01.003"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2016.03.022"}],"event":{"name":"SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"],"location":"Chicago Illinois","acronym":"SIGSPATIAL '19"},"container-title":["Proceedings of the 5th ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3356998.3365768","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3356998.3365768","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:37Z","timestamp":1750203877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3356998.3365768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,5]]},"references-count":21,"alternative-id":["10.1145\/3356998.3365768","10.1145\/3356998"],"URL":"https:\/\/doi.org\/10.1145\/3356998.3365768","relation":{},"subject":[],"published":{"date-parts":[[2019,11,5]]},"assertion":[{"value":"2020-07-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}