Open AccessMerged-LSTM and multistep prediction of daily chlorophyll-a concentration for algal bloom forecast
Author(s) -
Ho Mook Cho,
H Park
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/351/1/012020
Subject(s) - algal bloom , computer science , process (computing) , upstream (networking) , bloom , artificial intelligence , machine learning , data mining , phytoplankton , oceanography , ecology , telecommunications , nutrient , biology , geology , operating system
Algal blooms are significant environmental problems which threaten the water supply system and ecosystem. To manage the problem, the effective forecasting model is necessary, but it is still challenging to predict the algal bloom due to its uncertainty and complexity. To improve the prediction performance, this study proposed the advanced model based on LSTM networks. Merged-LSTM model contains the three parallel LSTM layers and merged layers which is available to use the additional data from the diverse sources without problem in the training process. To predict the chlorophyll-a of target area, data from an additional monitoring station in upstream and auxiliary environmental data were put into parallel layers as well as data from the target area. The prediction result of the proposed model outperforms the existing models, and also shows a better training process with larger data dimensions. The proposed model and its result also suggest that the possibility of prediction of algal bloom with more advanced models and corresponding data sources.