A Novel Deep Learning Algorithm for Groundwater Level Prediction based on Spatiotemporal Attention Mechanism

Chen, Chong; Zhou, Han; Hui, Zhang; Chen, Lulu; Zhu, Yada; Liang, Huaqing

doi:10.21203/rs.3.rs-59191/v1

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…AI consists of multidimensional systems combining various mathematical and statistical components and arithmetic and heuristic algorithms. AI has been extensively employed in science, engineering design, energy, robotics, and economics [16][17][18][19][20][21]. AI-based models (e.g., artificial neural network (ANN) and support vector machine (SVM)) have been used by some researchers worldwide for groundwater quality assessment and prediction [22][23][24][25][26].…”

Section: Literature Reviewmentioning

confidence: 99%

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Benaafi

Yassin

Usman³

et al. 2022

Sustainability

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Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.

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Section: Literature Reviewmentioning

confidence: 99%

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Benaafi

Yassin

Usman³

et al. 2022

Sustainability

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“…Li et al utilize a spatiotemporal enhanced network for predicting machine Remaining Useful Life [31]. The lack of ability to capture spatiotemporal features in the prediction model limits the accuracy of the flow prediction task and the adaptability to dynamic scenes [32,33]. Secondly, deep learning models often lack intuitive interpretability [34,35].…”

Section: Introductionmentioning

confidence: 99%

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

Hu,

Yu,

Yan

et al. 2024

Water

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The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model.

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Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru

Cárdenas

Altamirano

Bernaola

2022

Proceedings of the 7th Brazilian Technology Symposium (BTSym’21)

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A Novel Deep Learning Algorithm for Groundwater Level Prediction based on Spatiotemporal Attention Mechanism

Cited by 5 publications

References 15 publications

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

Black-Shoes-Merton Model and Neural Networks in River Level Prediction: Case Study on La Leche River - Peru

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