Surrogate optimization of deep neural networks for groundwater predictions

Juliane Müller (), Jangho Park, Reetik Sahu, Charuleka Varadharajan, Bhavna Arora, Boris Faybishenko and Deborah Agarwal
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Juliane Müller: Lawrence Berkeley National Laboratory
Jangho Park: Lawrence Berkeley National Laboratory
Reetik Sahu: Lawrence Berkeley National Laboratory
Charuleka Varadharajan: Lawrence Berkeley National Laboratory
Bhavna Arora: Lawrence Berkeley National Laboratory
Boris Faybishenko: Lawrence Berkeley National Laboratory
Deborah Agarwal: Lawrence Berkeley National Laboratory

Journal of Global Optimization, 2021, vol. 81, issue 1, No 8, 203-231

Abstract: Abstract Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models’ hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the “simplest” network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction.

Keywords: Hyperparameter optimization; Machine learning; Derivative-free optimization; Groundwater prediction; Surrogate models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10898-020-00912-0

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