RF-LSTM Prediction Model for Cyanobacteria Blooms in Dianchi Lake
In order to monitor and analyze the constraining effect of meteorological factors on the outbreak of blue-green algae blooms,estab-lish the response relationship between blue-green algae blooms and meteorological factors,and predict changes in blue-green algae biomass and distribution.Quantitatively evaluate the importance and contribution rate of meteorological factors through random forest algorithm,select meteorological factors,and construct a blue-green algae bloom prediction model using long short-term memory network.Selecting the annual values of normalized vegetation index in Dianchi Lake from 2000 to 2021 and meteorological data from Kunming Station during the same peri-od as the research objects,this study explores the feasibility of using the long short-term memory network RF-LSTM based on the random for-est algorithm to predict blue-green algae blooms,with normalized vegetation index as the indicator of changes in blue-green algae blooms.The results showed that compared with the single structure long short-term memory network model and the single algorithm random forest model,the RF-LSTM model's annual value prediction passed the 0.01 significance test,and the simulation accuracy reached 90.9%.The random for-est algorithm is beneficial for understanding the relationship between blue-green algae blooms and meteorological factors,selecting meteoro-logical factors with predictive performance,and thus improving the predictive ability of long short-term memory network models.
cyanobacterial bloomsmeteorological factorrandom forestlong and short term memory networkDianchi Lake
万方数据
维普
