International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1694-1698
Publisher
IEEE
Language
English
Type
Conference Paper
Abstract
Recently, harmful algae bloom in the river or lake become serious water quality issues as it can affect public health. In this paper, we introduce a hybrid model to predict Chlorophyll-a concentration at the non-monitoring spot by combining the existing simulation- and deep learning-based prediction methods. Our long short-term memory (LSTM) based deep learning algorithm models the correlation among the water quality data and provides the estimated water quality data. Then, the environmental fluid dynamics code (EFDC) simulation performs a spatiotemporal simulation of the region of interest, with the generated water quality data. The experimental results show that our prediction method achieves reliable performance on the Chlorophyll-a prediction at nonmonitoring spots with the estimated water quality data.
KSP Keywords
Environmental fluid dynamics code, Geum river, Harmful algae bloom, Hybrid Models, Learning-based, Long-short term memory(LSTM), Prediction methods, Quality Issues, Quality data, Region Of Interest(ROI), Water quality
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