9、李林-湖泊藻類水華的HEA模擬預警（29頁）.pdf

1、湖泊藻類水華的遺傳算法模型預測研究第五屆中國水環境模型與智能決策研討會.玉溪.2018.10.25李李 林、林、閃錕、閃錕、宋立榮宋立榮Hongqing Cao,Friedrich Recknagel主主要要內內容容13一、研究背景一、研究背景二二、遺傳算法簡介遺傳算法簡介三三、HEA模型用于藻類水華預測的案例模型用于藻類水華預測的案例3藻類水華預測的數據驅動模型方法 人工神經網絡(Artificial Neural Network，ANN)(Recknagel,F.,French,M.,Harkonen,P and K.Yabunaka 1997;Maier HR,Dandy GC,Burc

2、h MD,1998;Liu Y,Yao X,1999;Lee JHW,Fernando TMKG,Wong KTM,2004)4雜交演化算法(Hybrid Evolutionary Algorithm,HEA)基于時間序列水質參數的規則設定的水華藻類基于時間序列水質參數的規則設定的水華藻類HEA模擬原理圖模擬原理圖*Cao,H.,Recknagel,F.,Kim,B.and N.Takamura(2006)研究目的 能否用常規易檢測的理化參數來預測湖庫的藻能否用常規易檢測的理化參數來預測湖庫的藻類水華？類水華？HEAHEA模型預測藻類水華的效果如何？模型預測藻類水華的效果如何？與與ANNANN

3、方法相比，方法相比，HEAHEA模型有何優勢，能否篩選模型有何優勢，能否篩選關鍵的參數及其閾值？關鍵的參數及其閾值？滇池位置:云南昆明容量:15億方平均水深:4 m面積:300 km2流域面積:2920 km2Haigeng Bay1.滇池藻類水華的滇池藻類水華的HEA模擬預測模擬預測Variation of Total Phosphorus includes three stages:TP rose slowly from 1960 to 1990;TP was accelerated from 1991 to 2000;TP was fallen down since 2001.Varia

4、tion of Total Nitrogen includes two stages:Total nitrogen steady rise from 1960 to 2007;TN started to fall since 2008.滇池水質變化滇池水質變化TP TN 滇池外海葉綠素的變化滇池外海葉綠素的變化滇池藻類組成變化（滇池藻類組成變化（2008-2010）phylumgenusspecies滇池藻類滇池藻類HEA模擬的各參數變化范圍模擬的各參數變化范圍VariablesNameMeanMinMaxInput VariablesWater Temperature oC(WT)18.88

5、.029.5Secci Depth m(SD)26.3570Diss Oxygen mg/L(DO)8.83.216.0pH9.17.210.3Chemical Oxygen Demand mg/L(COD)28.81050Ammonia NH4-N mg/L(NH4-N)0.4802Filt Nitrite NO2-N mg/L(NO2-N)0.3800.5Filt Nitrate NO3-N mg/L(NO3-N)0.5602.1Total Nitrogen mg/L(TN)3.20.813Filt Reac Phosphorus mg/L(PO4-P)0.03200.16Total Ph

6、osphorus mg/L(TP)0.260.031.1Wind Direction(WD)6.7012Wind Speed m/s(WS)2.009.2Output VariablesChlorophyll-a ug/L(Chla)12052300Cyanophyceae mg/L870.03956Chlorophyceae mg/L8.30120Bacillariophyceae mg/L 2.3068Aphanizomenon mg/L10.10563Anabaena mg/L3.90186Microcystis mg/L29.70.02538Historical DataReal-ti

7、me in situWater QualityMeasurementsby Hydrolab DataSonde 5X Water Temperature CDissolved Oxygen mg/lpHTurbidity NTUAmmonium NH4 mg/lTotal Chlorophyll g/lData Acquisitionby HydrolabProcessMonitorData Merger and ValidationOnline DataLake Data Warehouse LDWEarly Warning for Operational Raw Water Contro

8、l if Cyanobacteria Bloom is ImminentTime SeriesChlorophyll-a g/lEvolutionaryModelling Real-Time ForecastingDays/Weeks AheadModelData Preprocessing ModuleForecasting ModuleConductivitymS/cmPhycocyanoin g/lOnline DataA.circinalis cells/mlC.raciborskii cells/mlCylindrospermopsin g/lHEA模擬預測的流程圖輸入水質理化參數：

9、COD,DO,NH4,NO2,NO3,pH,SD,SRP,TN,TP,Water temperature 氣象數據：風速,風向時間：2008.10-2010.9Output variables：Chla,Biomass of Algae(phylum,gerena,species)Hybrid Evolutionary Algorithms:輸出建模0100200300400500600700800Chla7d-predicted_ChlaIF(WT14.648)Chla=(TN*(TP*107.892)Chla=(WT-(TP*WT)*(TP*(55.408-WD)-WD)THENELSEH

10、EAHEA預測滇池葉綠素的最佳模型預測滇池葉綠素的最佳模型Best Model01002003004005006007008002008/10/12008/11/12008/12/12009/1/12009/2/12009/3/12009/4/12009/5/12009/6/12009/7/12009/8/12009/9/12009/10/12009/11/12009/12/12010/1/12010/2/12010/3/12010/4/12010/5/12010/6/12010/7/12010/8/12010/9/1Chla7d-predicted_ChlaIF(WT14.648)Chla=

11、(TN*(TP*107.892)Chla=(WT-(TP*WT)*(TP*(55.408-WD)-WD)THENELSE葉綠素葉綠素a a的的HEAHEA預測模型的靈敏度分析預測模型的靈敏度分析050100150200250050100TP:0.24-0.45WT:13.46-22.71WD:4.20-9.290100200300400500600700050100COD:30.99-42.53TN:5.75-7.40TP:0.59-0.81Sensitivity Analysis15Input Variables Selection frequency051015202530354045CO

12、D NH4 NO2 NO3 pHSDSRPTNTPWT WD WS0510152025302008/10/12008/12/12009/2/12009/4/12009/6/12009/8/12009/10/12009/12/12010/2/12010/4/12010/6/12010/8/1Water Temperature(Celsius)Water Temperature(Celsius)050100150200250300350400450Chlorophyll-a(ug/L)Chlorophyll-a(ug/L)y=14.059x-84.848R=0.551205010015020025

13、03003504004500102030Chlorophyll-a(ug/L)Chlorophyll-a(ug/L)線性(Chlorophyll-a(ug/L)從預測模型看水溫對葉綠素的影響從預測模型看水溫對葉綠素的影響WT01002003004005006007008002008/10/12008/11/12008/12/12009/1/12009/2/12009/3/12009/4/12009/5/12009/6/12009/7/12009/8/12009/9/12009/10/12009/11/12009/12/12010/1/12010/2/12010/3/12010/4/12010/

14、5/12010/6/12010/7/12010/8/12010/9/1Chla7d-predicted_Chla3d-predicted_Chla14d-predicted_ChlaItemselected best ruleset modeltotal errortotal R23d-Predicted ChlaIF(WT14.648)38.16620.90THEN Chla=(TN*(TP*107.892)ELSE Chla=(WT-(TP*WT)*(TP*(55.408-WD)-WD)7d-Predicted ChlaIF(WT=13.720)39.28480.89THEN Chla=(

15、WT*(15.728-WD)ELSE Chla=(TP*(81.224-ln(|NO3|)*WD)14d-Predicted ChlaIF(WT14.648)38.16620.90THEN Chla=(TN*(TP*107.892)ELSE Chla=(WT-(TP*WT)*(TP*(55.408-WD)-WD)3d-Predicted ChlaIF(WT=13.720)39.28480.89THEN Chla=(WT*(15.728-WD)ELSE Chla=(TP*(81.224-ln(|NO3|)*WD)14d-Predicted ChlaIF(WT=15.415)43.18300.87

16、THEN Chla=(exp(pH)-70.340)/64.110)+exp(ln(|WS|)ELSE Chla=(exp(pH)-313.306)/96.217)+(exp(TN)-(TP*(-550.157)/3.8)HEA 預測模型選擇的水溫數值與野外監測中Chla明顯增加時的水溫值非常接近，說明HEA模型具備選擇關鍵參數及其閾值的潛力。HEAHEA預測模型選擇參數的意義預測模型選擇參數的意義WT=14.8主要藻門類生物量提前7天的預測結果0501001502002503003502008/10/12008/12/12009/2/12009/4/12009/6/12009/8/12009

17、/10/12009/12/12010/2/12010/4/12010/6/12010/8/1Cyanopredicted_Cyano01020304050607080902008/10/12008/12/12009/2/12009/4/12009/6/12009/8/12009/10/12009/12/12010/2/12010/4/12010/6/12010/8/1Chloropredicted_Chloro05101520252008/10/12008/12/12009/2/12009/4/12009/6/12009/8/12009/10/12009/12/12010/2/12010/4/

18、12010/6/12010/8/1Bacillapredicted_BacillaItemThe best ruleset model for 7d-ahead-predictiontotal errortotal R2藍藻IF(SD146.831)OR(SD43.869)THENChloro=(WS*(pH/46.933)*SD)ELSEChloro=(ln(|(TN*17.252)|)*71.637)/(TP*(WS*SD)+COD)4.34130.91硅藻IF(exp(TN)=45.808)AND(WT=15.490)AND(SD=30.427)AND(DO=105.824)THENAn

19、abeana=(TP*(DO/WS)*(TP*147.343)-(WD+85.129)+ln(|NH4|)ELSE Anabeana=exp(exp(TP-NO3)/(NH4+TP)2.85220.98AphanizomenonIF(SRP+SD)=(-33.546)OR(SD=20.730)AND(SD=37.746)OR(WT20.154)THENT-Microcystis=(123.704-(COD/TP)ELSEMicrocystis=(TN-SD)-(TP/(TN-65.070)/exp(pH)15.11090.900204060801001201402008/10/12009/10

20、/1M.wesenbergiipredicted_M.wesenbergii051015202530354045502008/10/12008/12/12009/2/12009/4/12009/6/12009/8/12009/10/12009/12/12010/2/12010/4/12010/6/12010/8/1M.novacekiipredicted_M.novacekii01020304050602008/10/12008/12/12009/2/12009/4/12009/6/12009/8/12009/10/12009/12/12010/2/12010/4/12010/6/12010/

21、8/1M.viridispredicted_M.viridis024681012142008/10/12008/12/12009/2/12009/4/12009/6/12009/8/12009/10/12009/12/12010/2/12010/4/12010/6/12010/8/1M.aeruginosapredicted_M.aeruginosa常見微囊藻種類生物量的常見微囊藻種類生物量的7天預測天預測ItemThe best ruleset model for 7d-ahead-predictiontotal errortotal R2M.wesenbergiiIF(WT=17.750)

22、AND(SD146.464)OR(DO=12.290)AND(SD=19.662)OR(pH9.772)THEN M.viridis=(ln(|(COD/WD)|)+(ln(|(-73.683)/WD)|)+SD)ELSE M.viridis=(TP*(TN*4.493)+(COD/SD)+ln(|exp(NH4)|)5.23520.81M.novacekiiIF(WT22.693)THEN M.aeruginosa=(TP*(TP*(NO2*14.534)*WD)ELSEM.aeruginosa=(WS*57.755)*SRP*SRP)*(WS*ln(|SD|)*SRP)0.79310.90

23、常見微囊藻種類生物量的提前常見微囊藻種類生物量的提前7天預測的天預測的HEA模型模型模型及預警閾值設定3 Days Forecast ModelBest 1Best 2Best 3ThresholdAnabaena(mg/L)YYY10Aphanizomenon(mg/L)YYY15Microcystis(mg/L)YYY20Chlorophyll-a(ug/L)YYY307 Days Forecast ModelBest 1Best 2Best 3ThresholdAnabaena(mg/L)YYY10Aphanizomenon(mg/L)YXX15Microcystis(mg/L)YYY2

24、0Chlorophyll-a(ug/L)XXX3014 Days Forecast ModelBest 1Best 2Best 3ThresholdAnabaena(mg/L)YYY10Aphanizomenon(mg/L)YYY15Microcystis(mg/L)YYY20Chlorophyll-a(ug/L)YYY30小小 結結 雜交演化算法雜交演化算法HEA得到的滇池得到的滇池Chla、硅藻、綠藻、藍藻、主要藍藻屬、硅藻、綠藻、藍藻、主要藍藻屬（Anabeana、Aphanizomenon、Microcystis）和微囊藻的主要組成種類）和微囊藻的主要組成種類（Microcystis

25、novacekii、M.viridis、M.wesenbergii、M.aeruginosa）的預測模）的預測模型結果均較好（型結果均較好（r2 0.80）。）。提前提前3天、天、7天、天、14天的預測模型結果顯示，預測時間越短，預測模型精度越天的預測模型結果顯示，預測時間越短，預測模型精度越高。高。HEA 預測模型選擇的水溫數值與野外監測中預測模型選擇的水溫數值與野外監測中Chla明顯增加時的水溫值非常接明顯增加時的水溫值非常接近，說明近，說明HEA模型具備選擇關鍵參數及其閾值的潛力?？赏Ｐ途邆溥x擇關鍵參數及其閾值的潛力?？赏麨楣芾聿块T控制為管理部門控制藍藻水華危害提供決策信息支持。藍藻水

26、華危害提供決策信息支持。Wivenhoe Reservoir,QueenslandLocation:upper Brisbane RiverMax.Volume:1165 GLMax.Depth:79mCatchment Area:7020 km2大壩大壩2.澳大利亞澳大利亞WivenhoeWivenhoe水庫水庫Cylindropermopsis 的HEAHEA模擬模擬Cylindropermopsis 的HEAHEA預測模型預測模型Cylindropermopsis 的HEAHEA預測模型靈敏度分析預測模型靈敏度分析結 語1.將HEA用于三個湖庫的不同水華藻類生物量的預測都獲得了較好的效果

27、。從HEA模型的r2來看，滇池水華藍藻的預測模型最高（r20.80），其次是以色列Kinneret 湖甲藻（PeridiniumPeridinium）水華預測模型（0.64 r2 0.76），澳Wivenhoe水庫Cylindropermopsis 預測模型略低（0.59 r2 0.64）。這可能與不同湖泊富營養化程度、水華藻種類及生物量不同有關。2.HEA模型有助于篩選影響藻類水華的關鍵參數及其閾值，如水溫等，對水華預警與控制管理具有積極作用。致致 謝謝 滇池水專項課題（2008-2017）EES，University of Adelaide NSFC-ISF中國以色列國際合作項目（2015-2018）謝謝！敬請指正！李李 林林 18062034778中國科學院水生生物研究所中國科學院水生生物研究所藻類生物學與應用研究中心藻類生物學與應用研究中心