Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks

Gharehbaghi, Amin; Ghasemlounia, Redvan; Ahmadi, Farshad; Albaji, Mohammad

doi:10.1016/j.jhydrol.2022.128262

Cited by 21 publications

(3 citation statements)

References 38 publications

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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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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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“…Considering the significant time series characteristics of climate data, LSTM has demonstrated excellent predictive performance in the field of climate change (Xing et al, 2023). Gated recurrent units (GRUs) have shown considerable performance, but their structure is simpler, and their computational speed is higher (Gao et al, 2020;Gharehbaghi et al, 2022). The hybrid CNN-GRU prediction model improves prediction accuracy and generalization ability by combining the feature expression ability of CNNs with the time series memory advantage of GRUs (Yu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Guo,

He,

Wang

2023

Front. For. Glob. Change

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IntroductionAtmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.MethodsArtificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951–2000) and prediction data sets (2001–2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.Results and DiscussionThe number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 °C, −5.06 °C, and 42.44 °C, whereas those in 2040 were predicted to be 17.36 °C, −3.74 °C, and 42.68 °C, respectively. These results suggest that the climate is projected to continue warming in the future.

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“…In 2021, Hou et al (2021b) solved the problem of temperature prediction of switchgear equipment in substation by using long short-term memory (LSTM) network, and achieved good results, which opens the prelude of solving the problem of substation equipment temperature prediction with deep learning network. The gated recurrent unit (GRU) was proposed by Gharehbaghi et al (2022) and is an effective variant of LSTM ( Cao, Jiang & Gao, 2021 ; Yuan et al, 2022 ). In many cases, GRU and LSTM have the same excellent results, but GRU has fewer parameters, so it is relatively easy to train and the over fitting problem is lighter ( Cao, Jiang & Gao, 2021 ; Yuan et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Substation equipment temperature prediction based on multivariate information fusion and deep learning network

Sun

Liu

Wang

et al. 2022

PeerJ Computer Science

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Background Substation equipment temperature is difficult to achieve accurate prediction because of its typical seasonality, periodicity and instability, complex working environment and less available characteristic information. Methods To overcome these difficulties, a substation equipment temperature prediction method is proposed based on multivariate information fusion, convolutional neural network (CNN) and gated recurrent unite (GRU) in this article. Firstly, according to the correlation analysis including linear correlation mapping, autocorrelation function and partial autocorrelation function for substation equipment temperature data, the feature vectors from ambient, time and space are determined, that is the multivariate information fusion feature vector (denoted as MIFFV); secondly, the dimension of MIFFV is reduced by principal component analysis (PCA), extract some of the most important features and form the reduced feature vector (denoted as RFV); then, CNN is used for deep learning to extract the relationship between RFV and the high-dimensional space feature, and construct the high-dimensional feature vector of multivariate time series (denoted as HDFV); finally, the high-dimensional feature vector is used to train GRU deep learning network and predict the equipment temperature. Results A substation equipment in Taizhou City, Zhejiang Province is conducted by the method proposed in this article. Through the comparative experiment from the two aspects of features and methods, under the two prediction performance evaluation indexes of mean absolute percentage error (MAPE) and root mean square error (RSME), two main conclusions are drawn: (1) MIFFV from three aspects of ambient features, time features and space features have better prediction performance than the single feature vector and the combined feature vector of two aspects; (2) compared with other four related models under the same conditions, RFV is regarded as the input of the models, the proposed model has better prediction performance.

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Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks

Cited by 21 publications

References 38 publications

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

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

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Substation equipment temperature prediction based on multivariate information fusion and deep learning network

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