scholarly journals Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

2021 ◽  
Vol 9 ◽  
Author(s):  
Zehua Liang ◽  
Yaping Liu ◽  
Hongchang Hu ◽  
Haoqian Li ◽  
Yuqing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Table ◽  
North China Plain ◽  
North China ◽  
Short Term Memory ◽  
Anthropogenic Activities ◽  
Water Table Depth ◽  
Short Term ◽  
Long Short Term Memory ◽  
Over Exploitation

Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stationarity of water table depth data, the performance of NNs in areas of over-exploitation is challenging. Therefore, reducing data noise is an essential step before simulating the water table depth. This research proposed a novel method to model the non-stationary time series data of water table depth through combing the advantages of wavelet analysis and Long Short-Term Memory (LSTM) neural network (NN). A typical groundwater over-exploitation area, Baoding, North China Plain (NCP), was selected as a study area. To reflect the impact of anthropogenic activities, the variables harnessed to develop the model includes temperature, precipitation, evaporation, and some socio-economic data. The results show that decomposing the time series of the water table depth into three sub-temporal components by Meyer wavelets can significantly improve the simulation effect of LSTM on the water table depth. The average NSE (Nash-Sutcliffe efficiency coefficient) value of all the sites increased from 0.432 to 0.819. Additionally, a feedforward neural network (FNN) is used to compare forecasts over 12-months. As expected, wavelet-LSTM outperforms wavelet-FNN. As the prediction time increases, the advantages of wavelet-LSTM become more evident. The wavelet-LSTM is satisfactory for forecasting the water table depth at most in 6 months. Furthermore, the importance of input variables of wavelet-LSTM is analysed by the weights of the model. The results indicate that anthropogenic activities influence the water table depth significantly, especially in the sites close to the Baiyangdian Lake, the largest lake in the North China Plain. This study demonstrates that the wavelet-LSTM model provides an option for water table depth simulation and predicting areas of over-exploitation of groundwater.

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

2018 ◽  
Vol 561 ◽  
pp. 918-929 ◽  
Author(s):  
Jianfeng Zhang ◽  
Yan Zhu ◽  
Xiaoping Zhang ◽  
Ming Ye ◽  
Jinzhong Yang
Keyword(s):  
Water Table ◽  
Short Term Memory ◽  
Water Table Depth ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Agricultural Areas

scholarly journals Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

2020 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Real Time ◽  
Water Table ◽  
Short Term Memory ◽  
Water Table Depth ◽  
Alternative Methods ◽  
Short Term ◽  
Local Networks ◽  
Term Memory ◽  
Precipitation Anomalies ◽  
Long Short Term Memory

Abstract. Many European countries mainly rely on groundwater for domestic water use. Due to a scarcity of near real-time water table depth (wtd) observations, establishing a spatially consistent groundwater monitoring system at the continental scale is a challenge. Hence, it is necessary to develop alternative methods to estimate wtd anomalies (wtda) using other hydrometeorological observations routinely available near real-time. In this work, we explore the potential of Long Short-Term Memory (LSTM) networks to produce monthly wtda, using monthly precipitation anomalies (pra) as input. LSTM networks are a special category of artificial neural networks, useful in detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pra and wtda. To set up the methodology, spatio-temporally continuous data were obtained from daily terrestrial simulations (hereafter termed the TSMP-G2A data set) with a spatial resolution of 0.11°, ranging from the year 1996 to 2016. They were separated into a training set (1996–2012), a validation set (2013–2014), and a test set (2015–2016) to establish local networks at selected pixels across Europe. The modeled wtda maps from LSTM networks agreed well with TSMP-G2A wtda maps in 2003 and 2015 constituting drought years over Europe. Moreover, we categorized test performances of the networks based on yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (Sw), and soil type (St) and dominant plant functional type (PFT). Superior test performance was found at the pixels with wtd  200 mm, θ > 0.15 m3 m−3 and Sw 

scholarly journals Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

2021 ◽  
Vol 25 (6) ◽  
pp. 3555-3575
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Water Table ◽  
Groundwater Monitoring ◽  
Short Term Memory ◽  
Water Table Depth ◽  
Short Term ◽  
Term Memory ◽  
Precipitation Anomalies ◽  
Long Short Term Memory

Abstract. Many European countries rely on groundwater for public and industrial water supply. Due to a scarcity of near-real-time water table depth (wtd) observations, establishing a spatially consistent groundwater monitoring system at the continental scale is a challenge. Hence, it is necessary to develop alternative methods for estimating wtd anomalies (wtda) using other hydrometeorological observations routinely available near real time. In this work, we explore the potential of Long Short-Term Memory (LSTM) networks for producing monthly wtda using monthly precipitation anomalies (pra) as input. LSTM networks are a special category of artificial neural networks that are useful for detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pra and wtda. In the proposed methodology, spatiotemporally continuous data were obtained from daily terrestrial simulations of the Terrestrial Systems Modeling Platform (TSMP) over Europe (hereafter termed the TSMP-G2A data set), with a spatial resolution of 0.11∘, ranging from the years 1996 to 2016. The data were separated into a training set (1996–2012), a validation set (2013–2014), and a test set (2015–2016) to establish local networks at selected pixels across Europe. The modeled wtda maps from LSTM networks agreed well with TSMP-G2A wtda maps on spatially distributed dry and wet events, with 2003 and 2015 constituting drought years over Europe. Moreover, we categorized the test performances of the networks based on intervals of yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (Sw), soil type (St), and dominant plant functional type (PFT). Superior test performance was found at the pixels with wtd < 3 m, ET > 200 mm, θ>0.15 m3 m−3, and Sw<10 mm, revealing a significant impact of the local factors on the ability of the networks to process information. Furthermore, results of the cross-wavelet transform (XWT) showed a change in the temporal pattern between TSMP-G2A pra and wtda at some selected pixels, which can be a reason for undesired network behavior. Our results demonstrate that LSTM networks are useful for producing high-quality wtda based on other hydrometeorological data measured and predicted at large scales, such as pra. This contribution may facilitate the establishment of an effective groundwater monitoring system over Europe that is relevant to water management.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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