Enhanced Artificial Neural Networks for Salinity Estimation and Forecasting in the Sacramento-San Joaquin Delta of California
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 10
Abstract
Domain-specific architectures of artificial neural networks (ANNs) have been developed to estimate salinity levels for planning at key monitoring stations in the Sacramento-San Joaquin Delta, California. In this work, we propose three major enhancements to existing ANN architectures for purposes of training time reduction, estimation error reduction, and better feature extraction. Specifically, we design a novel multitask ANN architecture with shared hidden layers for joint salinity estimation at multiple stations, achieving a reduction of 90% training and inference time. As another major structural redesign, we replace predetermined preprocessing on input data by a trainable convolution layer. We further enhance the multitask ANN design and training for salinity forecasting. Test results indicate that these enhancements substantially improve the efficiency and expand the capacity of the current salinity modeling ANNs in the Delta. Our enhanced ANN design methodologies have the potential for incorporation into the current modeling practice and provide more robust and timely information to guide water resource planning and management in the Delta.
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Data Availability Statement
The following code and data that support the findings of this study are available from the corresponding author by request: Python code for training and evaluating the ANNs; input and output data used in ANN training and evaluation.
Acknowledgments
This work was supported in part by the research agreement No. 4600013184 from the California Department of Water Resources. The views and opinions expressed in this article are those of the authors and do not reflect the policy or position of their employers.
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Published In
Journal of Water Resources Planning and Management
Volume 147 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Aug 22, 2020
Accepted: Apr 29, 2021
Published online: Aug 9, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 9, 2022
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