An optimized indirect method to estimate groundwater table depth anomalies over Europe based on Long Short-Term Memory networks

2020 ◽  
Author(s):  
Yueling MA ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Term Memory ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

scholarly journals An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

Knowledge transfer from simulation to reality via Long Short-Term Memory networks:  Estimating groundwater table depth anomalies over Europe

2021 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Short Term Memory ◽  
Scientific Data ◽  
Groundwater Table ◽  
Short Term ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory ◽  
Simulation Results

<p>Near real-time groundwater table depth measurements are scarce over Europe, leading to challenges in monitoring groundwater resources at the continental scale. In this study, we leveraged knowledge learned from simulation results by Long Short-Term Memory (LSTM) networks to estimate monthly groundwater table depth anomaly (<em>wtd<sub>a</sub></em>) data over Europe. The LSTM networks were trained, validated, and tested at individual pixels on anomaly data derived from daily integrated hydrologic simulation results over Europe from 1996 to 2016, with a spatial resolution of 0.11° (Furusho-Percot et al., 2019), to predict monthly <em>wtd<sub>a</sub></em> based on monthly precipitation anomalies (<em>pr<sub>a</sub></em>) and soil moisture anomalies (<em>θ<sub>a</sub></em>). Without additional training, we directly fed the networks with averaged monthly <em>pr<sub>a</sub></em> and <em>θ<sub>a</sub></em> data from 1996 to 2016 obtained from commonly available observational datasets and reanalysis products, and compared the network outputs with available borehole <em>in situ</em> measured <em>wtd<sub>a</sub></em>. The LSTM network estimates show good agreement with the <em>in situ</em> observations, resulting in Pearson correlation coefficients of regional averaged <em>wtd<sub>a</sub></em> data in seven PRUDENCE regions ranging from 42% to 76%, which are ~ 10% higher than the original simulation results except for the Iberian Peninsula. Our study demonstrates the potential of LSTM networks to transfer knowledge from simulation to reality for the estimation of <em>wtd<sub>a</sub></em> over Europe. The proposed method can be used to provide spatiotemporally continuous information at large spatial scales in case of sparse ground-based observations, which is common for groundwater table depth measurements. Moreover, the results highlight the advantage of combining physically-based models with machine learning techniques in data processing.</p><p> </p><p>Reference:</p><p>Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S. (2019). Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation. Scientific Data, 6(1).</p>

Forecasting Net Groundwater Depletion in Well Irrigation Areas with Long Short-term Memory Networks

2020 ◽  
Author(s):  
Li Yuheng ◽  
Tang Lihua
Keyword(s):  
Hydrological Model ◽  
Short Term Memory ◽  
Irrigation Management ◽  
Groundwater Table ◽  
Groundwater Depletion ◽  
Short Term ◽  
Term Memory ◽  
Groundwater Use ◽  
Freshwater Budget ◽  
Long Short Term Memory

<p>Due to the scarcity of available surface water, many irrigated areas in North China Plain (NCP) heavily rely on groundwater, which has resulted in groundwater overexploitation and massive environmental impacts, such as groundwater depression core and land subsidence. The net groundwater depletion, one of the groundwater indicators, means the actual groundwater consumption for human impact. This indicator is quite essential for the evaluation of the effects of agricultural activities in well irrigation areas. However, net depletion forecasts, which can help inform the management of well irrigation areas, are generally unavailable with easy methods. Therefore, this study explored machine learning models, Long Short-term Memory (LSTM) networks, to forecast net groundwater depletion in well irrigation counties, Hebei Province. Firstly, Luancheng county was selected to construct the forecasting model. The training dataset was prepared by collecting the measured precipitation, remote sensing evaporation and groundwater table from 2006-2017. Besides, an agro-hydrological model (Soil-Water-Atmosphere-Plant, SWAP) with an optimization tool (Parameter ESTimation, PEST) was used to calculate the net depletion, and an unsaturated-saturated zone water balance conceptual hydrological model was constructed to calculate the net groundwater use. Secondly, to determine the effect of training data type on model accuracy, freshwater budget (evaporation minus precipitation), change of groundwater table and net groundwater use were chosen as training inputs by analyzing related temporal variable characteristics of net groundwater depletion. The response time of training inputs with net groundwater depletion were also approximated with highest cross-correlation value (CCF). Then, by circular bootstrapping methods to enlarge the Luancheng datasets from 2006-2016, the annual and monthly model for forecasting the net depletion were respectively trained with enlarged Luancheng datasets. Additionally, to test the model’s ability to predict the net groundwater depletion in other well irrigation areas with the similar rule of groundwater depletion, the annual and monthly forecasting scenarios were also carried out in the adjacent county, Zhaoxian. The results showed that both of the monthly and annual models estimating the groundwater net depletion had good performance in Zhaoxian from 2006-2017, with NSE of 0.91 and 0.81, respectively. According to the modelling results, further analysis showed that groundwater depletion in research counties mainly occurred in spring (March to May) and winter (December to February). In addition, the major factor leading to groundwater depletion in spring and winter was freshwater budget; while in summer and autumn, soil moisture determined the depletion activity. These results demonstrate the feasible use of LSTM networks to create annual and monthly forecasts of net groundwater depletion in well irrigation areas with similar depletion rule, which can provide valuable suggestion to well irrigation management in NCP within a challenging environment.</p><p><strong>Keywords: net groundwater depletion; long short-term memory; well irrigation areas</strong></p>

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
Sign in / Sign up

Export Citation Format

Share Document