Version 1
: Received: 6 February 2023 / Approved: 6 February 2023 / Online: 6 February 2023 (07:59:07 CET)
Version 2
: Received: 16 April 2023 / Approved: 17 April 2023 / Online: 17 April 2023 (07:21:31 CEST)
How to cite:
Khosravi, M.; Duti, B.M.; Yazdan, M.M.S.; Ghoochani, S.; Nazemi, N.; Shabanian, H. Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network. Preprints2023, 2023020086. https://doi.org/10.20944/preprints202302.0086.v2
Khosravi, M.; Duti, B.M.; Yazdan, M.M.S.; Ghoochani, S.; Nazemi, N.; Shabanian, H. Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network. Preprints 2023, 2023020086. https://doi.org/10.20944/preprints202302.0086.v2
Khosravi, M.; Duti, B.M.; Yazdan, M.M.S.; Ghoochani, S.; Nazemi, N.; Shabanian, H. Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network. Preprints2023, 2023020086. https://doi.org/10.20944/preprints202302.0086.v2
APA Style
Khosravi, M., Duti, B.M., Yazdan, M.M.S., Ghoochani, S., Nazemi, N., & Shabanian, H. (2023). Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network. Preprints. https://doi.org/10.20944/preprints202302.0086.v2
Chicago/Turabian Style
Khosravi, M., Neda Nazemi and Hanieh Shabanian. 2023 "Simultaneous Prediction of Stream-Water Variables Using Multivariate Multi-Step Long Short-Term Memory Neural Network" Preprints. https://doi.org/10.20944/preprints202302.0086.v2
Abstract
Multivariate predictive analysis of the Stream-Water (SW) parameters (discharge, water level, temperature, dissolved oxygen, pH, turbidity, and specific conductance) is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the meteorological and climatic features have a significant impact on the temporal distribution of the SW variables in recent days making the SW variables forecasting even more complicated for diversified water-related issues. To predict the SW variables, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-informed analysis and prediction, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, a comprehensive Explorative Data Analysis (EDA) and feature engineering were performed to prepare the dataset to obtain the best performance of the predictive model. Long Short-Term Memory (LSTM) neural network regression model is trained using over several years of daily data to predict the SW variables up to one week ahead of time (lead time) with satisfactory performance. The performance of the proposed model is found highly adequate through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and a set of error matrices. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time SW variables prediction.
Keywords
Deep neural network; long short-term memory; water quality; discharge; stream-water
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Shima Ghoochani
Commenter's Conflict of Interests: Author