Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Thanh, Hung Vo; AlRassas, Ayman Mutahar; Dahou, Abdelghani; Elaziz, Mohamed Abd

doi:10.1007/s11356-022-24326-5

Cited by 31 publications

(7 citation statements)

References 99 publications

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“…54 This implementation resulted in the utilization of the SSA search mechanism instead of the AO method, thereby improving the exploration process of AO. Furthermore, in another study, 55 a hybrid algorithm named AOSMA was introduced for hyperparameter optimization. AOSMA was developed by incorporating the search mechanism of the slime mold algorithm (SMA) into an enhanced version of the AO.…”

Section: Hyper-parameter Optimizationmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

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With the rapid development of telecommunication networks, the predictability of network traffic is of significant interest in network analysis and optimization, bandwidth allocation, and load balancing adjustment. Consequently, in recent years, significant research attention has been paid to forecasting telecommunication network traffic. Telecommunication traffic forecasting problems can be considered a time‐series problem, wherein periodic historical data is fed as the input to a model. Time‐series forecasting approaches are broadly categorized as statistical machine learning (ML) methods and their combinations. Statistical approaches forecast linear characteristics of time‐series data, unable to capture nonlinear and complex patterns. ML‐based approaches can model nonlinear characteristics of data. In recent years, hybrid approaches combining statistical and ML‐based approaches have been widely used to model linear and nonlinear data characteristics. However, the performance of these approaches highly depends on feature selection techniques and hyper‐parameter tuning of ML methods. A novel hybrid method is proposed for short‐term traffic forecasting based on feature selection and hyperparameter optimization to address this problem. It combines statistical and ML methods to model linear and nonlinear components of data. First, a novel feature selection technique, modified mutual information based on a linear combination of targets, is proposed to find the candidate input variables. Next, a combination of vector auto regressive moving average (VARMA), long short‐term memory (LSTM), and multilayer perceptron (MLP), called VARMA‐LSTM‐MLP forecaster, is suggested to forecast short‐term traffic. A hybrid metaheuristic algorithm, composed of firefly and BAT, is employed to find the optimal set of hyper‐parameter values. The proposed method is assessed by a real‐world dataset containing Tehran city's daily telecommunication data in IRAN. The evaluation results demonstrate that the proposed method outperforms the existing methods in terms of mean squared error and mean absolute error.

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Section: Hyper-parameter Optimizationmentioning

confidence: 99%

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Alizadeh,

Beheshti,

Ramezani

et al. 2023

Trans Emerging Tel Tech

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“…The LSTM is a type of recurrent neural network that utilizes information gates and advanced structures to predict outputs [21]. The LSTM automatically extracts relevant features from sequences without the need for manual feature engineering.…”

Section: Mathematical Model Of the Lstmmentioning

confidence: 99%

A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

Ehteram,

Banadkooki

2023

Water

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Groundwater level (GLW) prediction is essential for monitoring water resources. Our study introduces a novel model called convolutional neural network (CNN)–long short-term memory neural network (LSTM)–Multiple linear regression (MLR) for groundwater level prediction. We combine two deep learning models with the MLR model to predict GWL and overcome the limitations of the MLR model. The current paper has several innovations. Our study develops an advanced hybrid model for predicting groundwater levels (GWLs). The study also presents a novel feature selection method for selecting optimal input scenarios. Finally, an advanced method is developed to examine the impact of inputs and model parameters on output uncertainty. The current paper introduces the gannet optimization algorithm (GOA) for choosing the optimal input scenario. A CNN-LSTM-MLR model (CLM), CNN, LSTM, MLR model, CNN-MLR model (CNM), LSTM-MLR model (LSM), and CNN-LSTM model (CNL) were built to predict one-month-ahead GWLs using climate data and lagged GWL data. Output uncertainty was also decomposed into parameter uncertainty (PU) and input uncertainty (IU) using the analysis of variance (ANOVA) method. Based on our findings, the CLM model can successfully predict GWLs, reduce the uncertainty of CNN, LSTM, and MLR models, and extract spatial and temporal features. Based on the study’s findings, the combination of linear models and deep learning models can improve the performance of linear models in predicting outcomes. The GOA method can also contribute to feature selection and input selection. The study findings indicated that the CLM model improved the training Nash–Sutcliffe efficiency coefficient (NSE) of the CNL, LSM, CNM, LSTM, CNN, and MLR models by 6.12%, 9.12%, 12%, 18%, 22%, and 30%, respectively. The width intervals (WIs) of the CLM, CNL, LSM, and CNM models were 0.03, 0.04, 0.07, and, 0.12, respectively, based on IU. The WIs of the CLM, CNL, LSM, and CNM models were 0.05, 0.06, 0.09, and 0.14, respectively, based on PU. Our study proposes the CLM model as a reliable model for predicting GWLs in different basins.

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“…Their results showed that extreme gradient boosting (XGBoost model) achieved a root-mean-square error (RMSE) of 0.0041. In addition, Al-qaness et al 58 estimated CO 2 trapping indexes in deep saline aquifers using a Long–Short-Term-Memory (LSTM) model. The developed swarm intelligence method (AOSMA) boosts the prediction capability of the LSTM model.…”

Section: Introductionmentioning

confidence: 99%

Toward Estimating CO₂ Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO₂ Dissolution Trapping in Saline Aquifers

Zou,

Zhu,

et al. 2024

ACS Omega

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Predicting carbon dioxide (CO2) solubility in water and brine is crucial for understanding carbon capture and storage (CCS) processes. Accurate solubility predictions inform the feasibility and effectiveness of CO2 dissolution trapping, a key mechanism in carbon sequestration in saline aquifers. In this work, a comprehensive data set comprising 1278 experimental solubility data points for CO2–brine systems was assembled, encompassing diverse operating conditions. These data encompassed brines containing six different salts: NaCl, KCl, NaHCO3, CaCl2, MgCl2, and Na2SO4. Also, this databank encompassed temperature spanning from 273.15 to 453.15 K and a pressure range spanning 0.06–100 MPa. To model this solubility databank, cascade forward neural network (CFNN) and generalized regression neural network (GRNN) were employed. Furthermore, three optimization algorithms, namely, Bayesian Regularization (BR), Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton, and Levenberg–Marquardt (LM), were applied to enhance the performance of the CFNN models. The CFNN-LM model showcased average absolute percent relative error (AAPRE) values of 5.37% for the overall data set, 5.26% for the training subset, and 5.85% for the testing subset. Overall, the CFNN-LM model stands out as the most accurate among the models crafted in this study, boasting the highest overall R 2 value of 0.9949 among the other models. Based on sensitivity analysis, pressure exerts the most significant influence and stands as the sole parameter with a positive impact on CO2 solubility in brine. Conversely, temperature and the concentration of all six salts considered in the model exhibited a negative impact. All salts exert a negative impact on CO2 solubility due to their salting-out effect, with varying degrees of influence. The salting-out effects of the salts can be ranked as follows: from the most pronounced to the least: MgCl2 > CaCl2 > NaCl > KCl > NaHCO3 > Na2SO4. By employing the leverage approach, only a few instances of potential suspected and out-of-leverage data were found. The relatively low count of identified potential suspected and out-of-leverage data, given the expansive solubility database, underscores the reliability and accuracy of both the data set and the CFNN-LM model’s performance in this survey.

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Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

Cited by 31 publications

References 99 publications

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

Toward Estimating CO₂ Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO₂ Dissolution Trapping in Saline Aquifers

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Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

Cited by 31 publications

References 99 publications

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

Toward Estimating CO2 Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO2 Dissolution Trapping in Saline Aquifers

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Toward Estimating CO₂ Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO₂ Dissolution Trapping in Saline Aquifers