Leveraging Artificial Neural Networks for Modeling Hydrogeological Time Series

Bei der Lösung globaler Herausforderungen, wie der nachhaltigen Bewirtschaftung und Nutzung der verfügbaren Grundwasserressourcen, ist die Entwicklung neuer, effizienter und leicht übertragbarer Modellierungsansätze von entscheidender Bedeutung. Hierfür bieten sich vor allem künstliche neuronale Netze (KNN) an, die als Verfahren des maschinellen Lernens selbstständig relevante Zusammenhänge aus größeren Datensätzen geeigneter Parameter lernen und nutzen können. Die vorliegende Arbeit untersucht die Nutzung von KNN zu Modellierung und Vorhersage von hydrogeologischen Zeitreihen. In vier Studien, die den Hauptteil dieser Arbeit bilden, werden verschiedene Fragestellungen entwickelt und deren Lösbarkeit mit Hilfe von KNN demonstriert. Das Clustern von Ganglinien ist eine Möglichkeit räumliche und zeitliche Muster der Grundwasserdynamik zu erkennen. Dies ist wichtig um Aquifere zu charakterisieren, Einflussfaktoren zu identifizieren und effektive Bewirtschaftungsmethoden zu entwickeln. Aus diesen Gründen wird in der ersten Studie auf Basis von Self-Organizing Maps ein Clustering Verfahren entwickelt, mit dessen Hilfe sich in heterogenen Datensätzen von Grundwasserganglinien solche mit ähnlicher Dynamik gruppieren lassen. Das Verfahren nutzt zur Charakterisierung der Grundwasserdynamik sogenannte Features, die auch die Verarbeitung von Ganglinien mit variabler Datenqualität ermöglichen. Anhand eines Datensatzes von ca. 1800 wöchentlichen Ganglinien wird die Anwendung im Oberrheingraben in Deutschland und Frankreich erfolgreich demonstriert. Eine Analyse der Clusterergebnisse zeigt, dass sich externe Einflussfaktoren räumlich und zeitlich komplex überlagern und eine Trennung häufig nicht möglich ist. Dennoch sind einige Cluster eindeutig auf externe Faktoren (z.B. Grundwasserbewirtschaftung) zurückzuführen. Es folgt ein detaillierter Vergleich verschiedener KNN Modelle zur Grundwasserstandsvorhersage. Untersucht werden hierbei Nonlinear Autoregressive Models with Exogenous Inputs (NARX), Long Short-Term Memory Networks (LSTM) und Convolutional Neural Networks (CNN) sowohl jeweils für Einzelwert- als auch Sequenzvorhersagen. Als Eingangsdaten werden nur wenige, aber dafür weithin verfügbare und leicht zu messende meteorologische Parameter verwendet, wodurch die breite Übertragbarkeit des Ansatzes gewährleistet ist. Es zeigt sich, dass alle Modelltypen grundsätzlich gute Prognoseeigenschaften aufweisen und NARX hierbei in der Regel die präzisesten Vorhersagen treffen, dicht gefolgt von CNNs. Für die praktische Anwendbarkeit zeigen CNNs insgesamt das größte Potenzial, da diese eine geringere Abhängigkeit von der pseudorandomisierten Netzinitialisierung als NARX sowie eine vielfach höhere Berechnungsgeschwindigkeit aufweisen als beide rekurrenten Alternativen. Dabei erreichen CNNs dennoch eine hohe Güte und sind gleichzeitig flexibel implementierbar. CNNs bilden daher die Grundlage für weitere untersuchte Fragestellungen. Die nachfolgende Studie untersucht die Entwicklung der Grundwasserstände in Deutschland im Kontext des Klimawandels. Hierfür werden auf Basis von CNNs und anhand von Temperatur und Niederschlag aus drei Klimaszenarien (RCP2.6, 4.5 und 8.5) die zukünftigen Grundwasserstände an 118 ausgewählten Messstellen in Deutschland modelliert und der direkte Einfluss des zukünftigen Klimas abgeschätzt. Wichtige sekundäre Faktoren wie anthropogene Einflüsse, werden jedoch nicht in die Simulationen mit einbezogen. Unter RCP8.5 (pessimistisches Szenario) sind flächenhaft und ausgeprägt fallende Grundwasserstände zu erwarten, mit einem räumlichen Muster von stärkeren Abnahmen vor allem in Nord- und Ostdeutschland. Ebenfalls abnehmende Trends zeigen die Ergebnisse für die optimistischeren Szenarien RCP2.6 und RCP4.5, jedoch mit vergleichsweise wenig signifikanten Veränderungen. Hier wird der positive Einfluss der verminderten Treibhausgasemissionen deutlich, jedoch werden auch noch für das optimistischste Szenario RCP2.6 in einigen Projektionen deutschlandweit abnehmende Grundwasserstände festgestellt. Abschließend stehen Karstquellschüttungen im Fokus der Arbeit. Zur Modellierung werden zum einen die vorhandenen CNN Ansätze herangezogen, zum anderen wird ein ebenfalls auf CNNs basierender 2D-Ansatz entwickelt, der die direkte Verarbeitung von flächenhaften Rasterdaten als Inputs erlaubt. Hierdurch lässt sich vielfach das Problem der ungenügenden Datenverfügbarkeit von meteorologischen Eingabedaten im Einzugsgebiet lösen. Beide Ansätze zeigen in allen Testgebieten sehr gute Ergebnisse und übertreffen teils die Ergebnisse bereits existierender Modelle. Der direkte Vergleich zwischen herkömmlichem und flächenhaftem Modellierungsansatz erlaubt kein abschließendes Urteil zur Überlegenheit einer der beiden Ansätze hinsichtlich der Genauigkeit der Ergebnisse. Die räumliche und zeitliche Vollständigkeit der Eingabedaten ist jedoch ein schwerwiegender Vorteil des flächenhaften Ansatzes. Weiterhin zeigt der flächenhafte Ansatz Potenzial für die Lokalisierung und, bei entsprechender Datenverfügbarkeit und Weiterentwicklung des Ansatzes, auch für die Abgrenzung von Quelleinzugsgebieten im Karst

Deep Learning based assessment of groundwater level development in Germany until 2100

Clear signs of climate stress on groundwater resources have been observed in recent years even in generally water-rich regions such as Germany. Severe droughts, resulting in decreased groundwater recharge, led to declining groundwater levels in many regions and even local drinking water shortages have occurred in past summers. We investigate how climate change will directly influence the groundwater resources in Germany until the year 2100. For this purpose, we use a machine learning groundwater level forecasting framework, based on Convolutional Neural Networks, which has already proven its suitability in modelling groundwater levels. We predict groundwater levels on more than 120 wells distributed over the entire area of Germany that showed strong reactions to meteorological signals in the past. The inputs are derived from the RCP8.5 scenario of six climate models, pre-selected and pre-processed by the German Meteorological Service, thus representing large parts of the range of the expected change in the next 80 years. Our models are based on precipitation and temperature and are carefully evaluated in the past and only wells with models reaching high forecasting skill scores are included in our study. We only consider natural climate change effects based on meteorological changes, while highly uncertain human factors, such as increased groundwater abstraction or irrigation effects, remain unconsidered due to a lack of reliable input data. We can show significant (p<0.05) declining groundwater levels for a large majority of the considered wells, however, at the same time we interestingly observe the opposite behaviour for a small portion of the considered locations. Further, we show mostly strong increasing variability, thus an increasing number of extreme groundwater events. The spatial patterns of all observed changes reveal stronger decreasing groundwater levels especially in the northern and eastern part of Germany, emphasizing the already existing decreasing trends in these region

Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX

It is now well established to use shallow artificial neural networks (ANN) 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, especially shallow recurrent networks frequently seem to be excluded from the study design despite 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 ANN namely nonlinear autoregressive networks with exogenous inputs (NARX), and popular state-of-the-art DL-techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN). We compare both the performance on 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. We observe that 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; however, LSTMs and CNNs might perform substantially better with a larger data set, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though

Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles

Hydrograph clustering helps to identify dynamic patterns within aquifers systems, an important foundation of characterizing groundwater systems and their influences, which is necessary to effectively manage groundwater resources. We develope an unsupervised modeling approach to characterize and cluster hydrographs on regional scale according to their dynamics. We apply feature-based clustering to improve the exploitation of heterogeneous datasets, explore the usefulness of existing features and propose new features specifically useful to describe groundwater hydrographs. The clustering itself is based on a powerful combination of Self-Organizing Maps with a modified DS2L-Algorithm, which automatically derives the cluster number but also allows to influence the level of detail of the clustering. We further develop a framework that combines these methods with ensemble modeling, internal cluster validation indices, resampling and consensus voting to finally obtain a robust clustering result and remove arbitrariness from the feature selection process. Further we propose a measure to sort hydrographs within clusters, useful for both interpretability and visualization. We test the framework with weekly data from the Upper Rhine Graben System, using more than 1800 hydrographs from a period of 30 years (1986-2016). The results show that our approach is adaptively capable of identifying homogeneous groups of hydrograph dynamics. The resulting clusters show both spatially known and unknown patterns, some of which correspond clearly to external controlling factors, such as intensive groundwater management in the northern part of the test area. This framework is easily transferable to other regions and, by adapting the describing features, also to other time series-clustering applications

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21(st) century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under different RCP scenarios (2.6, 4.5, 8.5). We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. While less pronounced and fewer significant trends can be found under RCP2.6 and RCP4.5, we detect significantly declining trends of groundwater levels for most of the sites under RCP8.5, revealing a spatial pattern of stronger decreases, especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century

Groundwater level forecasting with artificial neural networks: A comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

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

Modeling the discharge behavior of an alpine karst spring influenced by seasonal snow accumulation and melting based on a deep-learning approach

Karst systems are challenging to model due to their heterogeneous hydraulic properties resulting in highly variable discharge behavior. Distributed models can be applied to karst aquifers but require detailed system knowledge and extensive hydraulic parameter datasets; lumped-parameter models are less complex, but still require parametrization. In this work, we demonstrate the application of a data-driven approach to model the discharge behavior of the Aubach spring in the Gottesacker karst system in the northern Alps, a well-investigated study site for which previous models are available for comparison (Chen et al. 2018; Fandel et al. 2020). Our approach is based on convolutional neural networks (CNN), which have proved to be well suited for time series forecasting in water-related contexts like runoff modelling or groundwater level prediction (Wunsch et al.). The approach is comparably simple in terms of data requirements as we rely mainly on widely available and easy-to-measure parameters such as precipitation and temperature. By implementing Bayesian techniques (Monte-Carlo dropout) we are able to report the predictive uncertainty of the CNN based forecasts. Our results challenge existing modelling results based on lumped-parameter models in terms of common error measures such as Nash-Sutcliffe efficiency. Furthermore, we explore the important role of snow accumulation and melting by coupling our model with a snow-routine to better represent their influence on spring discharge and further improve model performance. Our results demonstrate that the presented machine-learning approach can be applied to simulate karst spring discharge and has certain advantages in comparison with conventional karst modelling approaches, which require hydraulic parameters that are often not available

Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices

The prediction of groundwater nitrate concentration\u27s response to geo-environmental and human-influenced factors is essential to better restore groundwater quality and improve land use management practices. In this paper, we regionalize groundwater nitrate concentration using different machine learning methods (Random forest (RF), unimodal 2D and 3D convolutional neural networks (CNN), and multi-stream early and late fusion 2D-CNNs) so that the nitrate situation in unobserved areas can be predicted. CNNs take into account not only the nitrate values of the grid cells of the observation wells but also the values around them. This has the added benefit of allowing them to learn directly about the influence of the surroundings. The predictive performance of the models was tested on a dataset from a pilot region in Germany, and the results show that, in general, all the machine learning models, after a Bayesian optimization hyperparameter search and training, achieve good spatial predictive performance compared to previous studies based on Kriging and numerical models. Based on the mean absolute error (MAE), the random forest model and the 2DCNN late fusion model performed best with an MAE (STD) of 9.55 (0.367) mg/l, R2 = 0.43 and 10.32 (0.27) mg/l, R2 = 0.27, respectively. The 3DCNN with an MAE (STD) of 11.66 (0.21) mg/l and largest resources consumption is the worst performing model. Feature importance learning from the models was used in conjunction with partial dependency analysis of the most important features to gain greater insight into the major factors explaining the nitrate spatial variability. Large uncertainties in nitrate prediction have been shown in previous studies. Therefore, the models were extended to quantify uncertainty using prediction intervals (PIs) derived from bootstrapping. Knowledge of uncertainty helps the water manager reduce risk and plan more reliably