Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 10, Pages 6925-6934Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3039272
Keywords
Forecasting; Predictive models; Load modeling; Pollution measurement; Data models; Correlation; Computational modeling; Data driven; deep learning (DL); empirical mode decomposition (EMD); ensemble learning; forecasting model; influent loads; multimodal learning; wastewater treatment plant (WWTP)
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Funding
- National Research Foundation of Korea - Korea government (MSIT) [NRF-2017R1E1A1A03070713]
- Korea Ministry of Environment (MOE) as Graduate School specialized in Climate Change
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This article introduces a hybrid influent forecasting model based on multimodal and ensemble-based deep learning, showing superior performance in predicting WWTP influent loads compared to five recurrent neural network-based reference models.
With the advent of the Industry 4.0 and the introduction of smart technologies in wastewater treatment plants (WWTP); forecasting influent loads is essential to regulate exaggerated operational strategies for WWTP. However, due to various water usage and sources, it is challenging to forecast the fluctuating influent loads. To deal with highly nonlinear and temporal-correlated characteristics of influent loads, in this article, we proposed hybrid influent forecasting model based on multimodal and ensemble-based deep learning (ME-DeepL). The proposed ME-DeepL forecasting model combines strength of multiple deep-learning algorithms in ensemble-learning architecture to handle propagated intrinsic sublayers by empirical mode decomposition of influent loads. Then, the proposed model was assessed to predict the loads on long-term (daily), and short-term (hourly) with multisteps forecast horizons. The experimental results revealed that ME-DeepL model exhibited superior forecasting performance in comparison to five recurrent neural network-based reference models, by capturing the informative features and temporal patterns from fluctuating influent loads.
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