Machine learning surrogates for efficient hydrologic modeling: Insights from stochastic simulations of managed aquifer recharge

• URL: http://arxiv.org/abs/2407.20902v2
• Date: Sun, 09 Feb 2025 20:06:31 GMT
• Title: Machine learning surrogates for efficient hydrologic modeling: Insights from stochastic simulations of managed aquifer recharge
• Authors: Timothy Dai, Kate Maher, Zach Perzan,
• Abstract summary: We show that machine learning surrogate models can achieve under 10% mean absolute percentage error. We apply this workflow to simulations of variably saturated groundwater flow at a prospective managed aquifer recharge site. ML surrogate models can achieve under 10% mean absolute percentage error and yield order-of-magnitude runtime savings.
• Score: 0.0
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• Abstract: Process-based hydrologic models are invaluable tools for understanding the terrestrial water cycle and addressing modern water resources problems. However, many hydrologic models are computationally expensive and, depending on the resolution and scale, simulations can take on the order of hours to days to complete. While techniques such as uncertainty quantification and optimization have become valuable tools for supporting management decisions, these analyses typically require hundreds of model simulations, which are too computationally expensive to perform with a process-based hydrologic model. To address this gap, we assess a hybrid modeling workflow in which a process-based model is used to generate an initial set of simulations and a machine learning (ML) surrogate model is then trained to perform the remaining simulations required for downstream analysis. As a case study, we apply this workflow to simulations of variably saturated groundwater flow at a prospective managed aquifer recharge site. We compare the accuracy and computational efficiency of several ML architectures, including deep convolutional networks, recurrent neural networks, vision transformers, and networks with Fourier transforms. Our results demonstrate that ML surrogate models can achieve under 10% mean absolute percentage error and yield order-of-magnitude runtime savings over process-based models. Building on these findings, we examine the impacts of key modeling choices on surrogate model accuracy and efficiency. Results show that a normalized loss function improves training stability, while min-max data normalization can significantly reduce error up to a factor of 10 when compared to other treatments such as Z-score and no normalization. Downsampling input features using an autoencoder also decreases memory requirements by training with tensors 4% their original size. By reducing computational costs and...

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