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1、Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/279559401CyanobacteriabloompredictionmodelandparametersoptimizationbasedongeneticalgorithmArticle·January2010CITATIONSREADS3234authors,including:JiacongHuangXiaodongW
2、uNanjingInstituteofGeographyandLimnologyChineseAcademyofSciences35PUBLICATIONS254CITATIONS96PUBLICATIONS722CITATIONSSEEPROFILESEEPROFILESomeoftheauthorsofthispublicationarealsoworkingontheserelatedprojects:PermafrostanditsenvironmentintheTibetanPlateauViewprojectLan
3、dSurfaceProcessandModelinginPermafrostRegionsonQTPViewprojectAllcontentfollowingthispagewasuploadedbyJiacongHuangon28September2017.Theuserhasrequestedenhancementofthedownloadedfile.生态学报2010,30(4):1003—1010ActaEcologicaSinica蓝藻水华预报模型及基于遗传算法的参数优化1,211,31黄佳聪,吴晓东,高俊峰,孔繁
4、翔(1.中国科学院南京地理与湖泊研究所,南京210008;2.中国科学院研究生院,北京100049)摘要:蓝藻水华预报是应对水危机,保障水资源供给的一项重要工作。以太湖北部三湾(竺山湖、梅梁湾、贡湖)为研究对象,采用动态空间环境建模技术,构建了蓝藻水华预报模型,并通过实地观测建立了模拟的初始参数集。利用2008年0409月太湖水环境、气象等实测数据,采用遗传算法优化叶绿素a浓度预报模型中敏感度较高的4个参数。研究结果表明,该模型在蓝藻水华空间分布的预报上达到了一定的精度;采用遗传算法能全面、高效地进行参数优化,降低了模拟结果的
5、相对残差,提高了模型预报精度。关键词:蓝藻水华;预报模型;动态空间环境模拟;参数优化;遗传算法;太湖Cyanobacteriabloompredictionmodelandparametersoptimizationbasedongeneticalgorithm1,211,31HUANGJiacong,WUXiaodong,GAOJunfeng,KONGFanxiang1NanjingInstituteofGeographyandLimnology,ChineseAcademyofSciences,Nangjing210008
6、,China2GraduateUniversityofChineseAcademyofSciences,Beijing100049,ChinaAbstract:Cyanobacteriabloompredictionisveryimportantforwatercrisisandwaterresourcesecurity.Techniqueofdynamicspatialenvironmentalmodellingisusedtodevelopcyanobacteriabloompredictionmodelusedinthr
7、eebays(MeiliangBay,ZhushanBay,GongBay)ofnorthernTaihuLake.Theinitialmodelparametersareobtainedfromfieldobservation.Thefourparametershighlysensitiveinchlorophyll2aconcentrationpredictionaredeterminedusingGeneticAlgorithmoptimizationtechnique.Theobservedfielddataofwat
8、erenvironmentandmeteorologicalconditionsinTaihuLakefromApriltoSeptember2008areusedforthispurpose.Theresultsshowedthat,GeneticAlgorithmisco