Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network

Ghasemlounia, Redvan; Gharehbaghi, Amin; Ahmadi, Farshad; Saadatnejadgharahassanlou, Hamid

doi:10.1016/j.compag.2021.106568

Cited by 37 publications

(6 citation statements)

References 34 publications

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“…BiLSTM consists of two LSTMs with opposite directions and has recently been shown to outperform traditional unidirectional ones. For example, Ghasemlounia et al (2021) found that, during calibration and validation periods, BiLSTM can predict groundwater level fluctuations better than unidirectional LSTM. Zhang et al (2023) found that BiLSTM has higher accuracy in rainfall prediction compared to LSTM.…”

Section: Introductionmentioning

confidence: 99%

Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21

Zhang,

Qi,

Liu

et al. 2024

Environ Model Assess

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Controlling non-point source (NPS) pollution is crucial for implementing water environment management, and simulating the water quality response to NPS pollution emission control schemes is of great importance. Variational mode decomposition (VMD) can overcome endpoint effects and modal aliasing issues, effectively separating intrinsic mode components. Bidirectional long short-term memory (BiLSTM) can fully mine the information contained in time series and has good predictive performance. MIKE21, when coupled with the Ecolab module, can well simulate the diffusion process of NPS pollution. The Weihe River water environment prediction model was constructed using VMD-BiLSTM and MIKE21, with ammonia nitrogen (NH 3 -N), total phosphorus (TP), and chemical oxygen demand (COD) as pollution indicators, showing the water quality response of the Weihe River within a few years after the implementation of agricultural and urban NPS pollution emission control schemes. Among them, the COD concentration decreased by up to 71.3%, the NH3-N concentration decreased by up to 31.4%, and the TP concentration decreased by up to 43.1%. The results show that the water quality of the Weihe River can be significantly improved by controlling NPS pollution emission, and reducing agricultural NPS pollution emission is key to decreasing ammonia nitrogen and total phosphorus concentrations and improving water quality.

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Section: Introductionmentioning

confidence: 99%

Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21

Zhang,

Qi,

Liu

et al. 2024

Environ Model Assess

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“…These methods are well known as the top adaptive ones suitable for finding complex and nonlinear indefinite patterns in large dimensional data. As scientists’ skill with these AI-based systems deepens, they are becoming more dependable, and now they are frequently utilized as robust approaches in different fields of water sciences to predict complex hydraulic and hydrological variables such as sugarcane growth based on climatological parameters (Taherei Ghazvinei et al 2018 ), daily dew point temperature (Qasem et al 2019 ), forecasting nitrate concentration as a water quality parameter (Latif et al 2020 ), inflow forecasting (Latif et al 2021a ), phosphate forecasting in reservoir water system (Latif et al 2021b ), daily streamflow time-series prediction (Latif and Ahmed 2021 ; Tofiq et al 2022 ), surface water quality status and prediction during movement control operation order under COVID-19 pandemic (Najah et al 2021 ), groundwater level fluctuations (Ghasemlounia et al 2021 ; Gharehbaghi et al 2022 ), discharge coefficient of a new type of sharp-crested V-notch weirs (Gharehbaghi and Ghasemlounia 2022 ), and dissolved oxygen prediction (Ziyad Sami et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Application of data-driven models to predict the dimensions of flow separation zone

Gharehbaghi

Ghasemlounia

Latif

et al. 2023

Environ Sci Pollut Res

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In this research, the effect of a submerged multiple-vane system on the dimensions of flow separation zone (DFSZ) is assessed via 192 measured datasets. The vanes’ shape comprised two segments, curved and flat plates which are located in the connection of main channel to the lateral intake channel with an angle of 55°. In this direction, a butterfly’s array for the vanes’ arrangement along with different main controlling factors such as distances of vanes along the flow ( δ l ), degree of curvature ( β ), and angles of attack to the local primary flow direction ( θ ) is utilized. Through capturing photos and utilizing AutoCAD and SURFER software, maximum relative length and width are calculated. Based on the experimental measurements, maximum percentage reduction of DFSZ, in comparison with the controlled test (without submerged vanes), is obtained with θ = 30°, β = 34°, and δ l = 10 cm with value of 78 and 76%, respectively. Moreover, several data-driven models, namely, gene expression programming (GEP), support vector regression (SVR), and a robust hybrid SVR with an ant colony optimization algorithm (ACO) (i.e., hybrid SVR-ACO model), are developed in order to predict DFSZ via the operative dimensionless variables realized by Spearman’s rho and Pearson’s coefficient processes. In accordance with the statistical metrics, model grading process, scatter plot, and the hybrid SVR(RBF)-ACO model are preferred as the best and most precise model to predict maximum relative length and width with a total grade ( TG ) of 6.75 and 5.8, respectively. The generated algebraic formula for DFSZ under the optimal scenario of GEP is equated with the corresponding measured ones and the results are within 0–10%.

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“…Deep learning (DEL) models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are useful in modeling groundwater dynamics because they can effectively capture spatial and temporal dependencies. Furthermore, DEL models can be trained using large datasets, which can enhance their precise and generalization performance (Ghasemlounia et al, 2021). The deep learning models have shown promising results in predicting groundwater levels.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Deep Learning model for Predicting Groundwater Level

Ehteram

Ghanbari-Adivi

2023

Preprint

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Groundwater level prediction is important for effective water management. Accurately predicting groundwater levels allows decision-makers to make informed decisions about water allocation, groundwater abstraction rates, and groundwater recharge strategies. Groundwater level prediction can also be used to develop more effective drought preparedness plans to mitigate the impact of water scarcity. In this study, we introduce a new model called self-attention (SA) temporal convolutional network (SATCN)-long short term memory neural network (SATCN-LSTM) model to predict groundwater level. The new model combines the advantages of the SATCN model and the LSTM model to overcome the limitations of the LSTM model. The SATCN model uses skip connections and self-attention mechanisms to overcome the vanishing gradient problem of the LSTM model, identify relevant and irrelevant data, and capture short-, and long-term dependencies of time series data. The new model was used to predict GWL in a large basin. Meteorological data were used to predict GWL. The SATCN-LSTM model outperformed the other models. The SATCN-LSTM model had the lowest mean absolute error (MAE) of 0.06, followed by the self-attention (SA) temporal convolutional network (SATCN) model with an MAE of 0.09. The SALSTM model had an MAE of 0.12, while the TCN-LSTM, TCN, and LSTM models had MAEs of 0.14, 0.15, and 0.17, respectively. The SATCN-LSTM model had the lowest root mean square error (RMSE) of 0.08, followed by SATCN with an RMSE of 0.11. The results of the SATCN-LSTM model provide valuable insights into the dynamics of groundwater systems. By accurately predicting groundwater levels, the SATCN-LSTM model can help ensure that groundwater resources are used sustainably and efficiently.

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Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network

Cited by 37 publications

References 34 publications

Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21

Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21

Application of data-driven models to predict the dimensions of flow separation zone

An Advanced Deep Learning model for Predicting Groundwater Level

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