# Surrogate Optimization of Deep Neural Networks for Groundwater Predictions

arXiv: https://arxiv.org/abs/1908.10947 · HTML: https://mlsanity.com/paper/1908.10947/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks](https://mlsanity.com/paper/2202.12441/) (2022)
- [HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization](https://mlsanity.com/paper/2110.01698/) (2021)
- [The phase unwrapping of under-sampled interferograms using radial basis function neural networks](https://mlsanity.com/paper/2210.10541/) (2022)
- [Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM](https://mlsanity.com/paper/2107.13870/) (2021)
- [Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks](https://mlsanity.com/paper/2201.10002/) (2022)
- [Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations](https://mlsanity.com/paper/2208.07715/) (2022)

## Abstract (arXiv)

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.