Abstract
Abstract. It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, shallow recurrent networks frequently seem to be excluded from the study design due to the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. Further, we also investigate the data dependency in terms of time series length of the different ANN architectures. For seq2val forecasts, NARX models on average perform best; however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.
Highlights
Groundwater is the only possibility for 2.5 billion people worldwide to cover their daily water needs (UNESCO, 2012). and at least half of the global population uses groundwater for drinking-water supplies (WWAP, 2015)
We aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent artificial neural networks (ANNs), namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs)
We showed that shallow neural networks, such as Nonlinear autoregressive exogenous model (NARX), should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though
Summary
Groundwater is the only possibility for 2.5 billion people worldwide to cover their daily water needs (UNESCO, 2012). and at least half of the global population uses groundwater for drinking-water supplies (WWAP, 2015). Groundwater is the only possibility for 2.5 billion people worldwide to cover their daily water needs (UNESCO, 2012). At least half of the global population uses groundwater for drinking-water supplies (WWAP, 2015). Groundwater constitutes for a substantial amount of global irrigation water (FAO, 2010), which altogether and among other factors such as population growth and climate change make it a vital future challenge to dramatically improve the way of using, managing, and sharing water (WWAP, 2015). Accurate and reliable groundwater level (GWL) forecasts are a key tool in this context, as they provide important information on the quantitative availability of groundwater and can form the basis for management decisions and strategies. A. Wunsch et al.: Groundwater level forecasting with LSTM, CNNs, and NARX
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