Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas

Zhang, Jianfeng; Zhu, Yan; Zhang, Xiaoping; Ye, Ming; Yang, Jinzhong

doi:10.1016/j.jhydrol.2018.04.065

Cited by 575 publications

(276 citation statements)

References 47 publications

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“…Recent study conducted by Lee et al [27] found the similar relationship between GWLs and stream level. Zhang et al [10] compared feedforward neural networks with LSTM in GWL prediction in the Hetao irrigation district, China. Contrary to our findings, their results suggest that the LSTM model is a better predictor of GWL modeling than the MLP.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Results indicated that the AI methods performed well in water-stressed conditions as compared to HYDRUS-2D. Therefore, AI methods provide promising tools for GWL predictions [10]. Several AI methods have been used by many researchers because of their simplicity and acceptable performance in different parts of the world [7].Deep learning has been used to solve real-world problems relating to multi-magnitude data [11], determining the bearing capacity of concrete-steel columns [12], soil liquefaction [13], and solving genetic algorithms [14].…”

mentioning

confidence: 94%

“…To overcome the vanishing gradient problems of RNNs, more advanced models such as LSTM were introduced, and these are capable of storing important information with long sequences. Recently, Zhang et al [10] conducted a similar study in which they compared LSTM with an MLP to predict GWLs in the Hetao irrigation district, China. Monthly water diversion, evaporation, precipitation, temperature, and time were used as input data to predict water table depth.…”

Section: Introductionmentioning

confidence: 99%

“…However, they did not include a detailed comparison of several input combinations for developing less data-intense models. Similarly, the more advanced time series models such as the CNN were not included [10].…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Afzaal

Farooque

Abbas

et al. 2019

Water

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Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada. Variables including stream level, stream flow, precipitation, relative humidity, mean temperature, evapotranspiration, heat degree days, dew point temperature, and evapotranspiration for the 2011-2017 period were used as input variables. Using a hit and trial approach and various hyperparameters, all ANNs were trained from scratched (2011)(2012)(2013)(2014)(2015) and validated (2016)(2017). The stream level was the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds (R 2 = 50.8 and 49.1%, respectively). The MLP performed better in validation for Baltic River and Long Creek watersheds (RMSE = 0.471 and 1.15, respectively). Increased number of variables from 1 to 4 improved the RMSE for the Baltic River watershed by 11% and for the Long Creek watershed by 1.6%. The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management.Water 2020, 12, 5 2 of 18 procedure that requires thorough knowledge of physical hydrological parameters, big data, hydrological models, model inputs, and the geometry of watersheds [3]. Aspects of hydrogeology-i.e., geological factors affecting the distribution and movement of groundwater underneath the soil surface-need to be properly understood when modeling GWLs and manipulating the modeling results. Watershed scale fluctuations in GWLs occur over a period of several decades, and the resulting cumulative effects on streamflow depletion may not be fully realized for years [4]. Resultantly, depending upon the distance of the pumping station from the stream and the geologic characteristics of the aquifer, the groundwater system may take decades to recover from streamflow depletion caused by intermittent pumping. Components of the surface-and the sub-surface physical hydrology of a watershed-i.e., streamflow and groundwater flow, respectively-are interconnected, making the stream-aquifer interaction one of the key processes governing the groundwater flow pattern in a watershed [1]. Groundwater fluctuations affect streamflow and vice versa, as the pumping wells capture groundwater that would otherwise discharge to connected streams, rivers, and other surface-water bodies [4]. Francis [5] reported that, in typical watersheds of Prince Edward Island, the base flow represents almost 80% of the streamflow in the late summer and fall months. Stream length in these island watersheds ranges from less than 1 km to 2...

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Section: Model Evaluationmentioning

confidence: 99%

mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Afzaal

Farooque

Abbas

et al. 2019

Water

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Bibliography

2021

Deep Learning for the Earth Sciences

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Using LSTM to monitor continuous discharge indirectly with electrical conductivity observations

Chang

Mewes

Hartmann

2021

Preprint

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Due to EC's easy recordability and the existence of a strong correlation between EC and discharge in certain catchments, EC is a potential predictor of discharge. This potential has yet to be widely addressed. In this paper, we investigate the feasibility of using EC as a proxy for long-term discharge monitoring in a small karst catchment where EC always shows a negative correlation with the spring's discharge. Given their complex relationship, a special machine learning architecture, LSTM (Long Short Term Memory), was used to handle the mapping from EC to discharge. The results indicate, based on LSTM, that the spring's discharge can be predicted well with EC, particularly in storms when the dilution dominates the EC dynamic; however, the prediction may have relatively large uncertainties in the small or middle recharge events. A small number of discharge observations are sufficient to obtain a robust LSTM for the long-term discharge prediction from EC, indicating the practicality of recording EC in ungauged catchments for indirect discharge monitoring. Our study also highlights that the random or fixed-interval discharge measurement strategy, which covers various climate conditions, is more informative for LSTM to give robust predictions. While our study is implemented in a karst catchment, the method is also suitable for non-karst catchments where there is a strong correlation between EC and discharge.

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Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas

Cited by 575 publications

References 47 publications

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Bibliography

Using LSTM to monitor continuous discharge indirectly with electrical conductivity observations

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