Surrogate optimization of deep neural networks for groundwater predictions
Surrogate optimization of deep neural networks for groundwater predictions
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DOI：
10.1007/s10898-020-00912-0
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发表时间：
2020-05-26
影响因子：
1.8
通讯作者：
Agarwal, Deborah
中科院分区：
文献类型：
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作者：
Mueller, Juliane;Park, Jangho;Agarwal, Deborah
在不断变化的气候条件下对地下水资源进行可持续管理，需要应用可靠和准确的地下水位预测。机械性的多尺度、多物理模拟模型往往很难用于这一目的，特别是对于无法访问复杂计算资源和数据的地下水管理人员。为此，我们分析了四种用于地下水位预测的现代深度学习计算模型的适用性和性能。我们比较了三种优化模型超参数的方法，包括两种基于代理模型的算法和一种随机抽样方法。通过对美国加利福尼亚州巴特县地下水位的预测，考虑了径流、降水和环境温度的时间变异性，对模型进行了检验。我们的数值研究表明，对超参数的优化可以使所有模型都有相当准确的性能(地下水预测的均方根误差小于等于2米)，但在预测精度和求解时间方面，最简单的网络，即多层感知器(MLP)在学习和预测地下水数据方面总体上比更先进的长期短期记忆网络或卷积神经网络更好，使MLP成为适合于地下水预测的候选网络。
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.