Short-term Memory Method Research Articles

NH4 Modelling with ARIMA and LSTM

AI-Mining is a prototype designed to detect various environmental gases, including CO2, NH3, NH4, and hydrogen, alongside temperature, pressure, and humidity. This study emphasizes the importance of modeling NH4 time series data due to its critical role in environmental and health monitoring. Accurate NH4 predictions facilitate early pollution detection and timely greenhouse gas control interventions. The study investigates the effectiveness of AI-Mining in detecting and predicting gas levels, focusing on data collection and analysis. Initial data analysis employed the Autoregressive Moving Average (ARIMA) model, specifically ARIMA (1,1,1), described by the equation yt = 0.0311 - 0.0750yt-1 + 0.3842εt-1. Despite its use, ARIMA's Root Mean Square Error (RMSE) performance was found lacking compared to more advanced methods. Given the classification of the obtained data as big data and time series, the Long Short-Term Memory (LSTM) method was also applied. The LSTM model initially used two layers with tanh and relu activation functions, and its performance was further explored by adding a third layer and varying the number of neurons (64, 128, and 256). The Adam optimizer was consistently used across all LSTM variations. Results indicated that increasing layers and neurons did not significantly impact LSTM's performance, with RMSE values around 0.023. However, LSTM consistently outperformed ARIMA in prediction accuracy, highlighting its robustness and reliability. Consequently, the study recommends using LSTM for predicting other recorded data in AI-Mining, underscoring its superiority in handling complex environmental datasets.

Review of Biomass Gasification and Pyrolysis Process Based on Long Short-Term Memory Network

Abstract. With the global energy crisis become escalated day by day, finding an effective way of exploring the renewable energy has become an important direction as its widely resources, comparatively low environment impact. This paper has concluded the biomass pyrolysis and gasification process based on long- and short-term memory (LSTM) method, including the LSTM principle, current application, faced challenges and future development. LSTM as a kind of special recurrent neural network, which could effectively solve the gradient vanish and explosion problem in time-series data. Research shows that, LSTM has an excellent performance in terms of reaction condition and optimization, which could be able to capture the relationship between complex variables, the pyrolysis efficiency could be improved, and prediction accuracy could over 90%. During the data preprocessing and model training stage, it is vital to make sure the quality of data, including data normalisation, outlier handling and time series splitting, etc. Through these steps, a better prediction accuracy could be achieved by LSTM. At the same time, the real application cases of LSTM used in biomass pyrolysis process have been discussed, it is shown that the combination of LSTM and other machine learning techniques could obviously improve the system stability and product quality. In addition, this paper has also looked forward to the enhancement of model explainability and the prospect of uncertainty quantification, which is helpful to improve the LSTM application and practical value.

Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety

The fishing industry has a large role in the Indonesian economy, with potential profits in 2020 of around US$ 1.338 billion. Tuban Regency is one of the regions in East Java that contributes to the fisheries sector. Fisheries relate to the work of fishermen. Accidents in shipping are still a major concern. One of the natural factors that influence shipping accidents is the height of the waves. Fisherman safety regulations have been established by the Ministry of Maritime Affairs and Fisheries and the Meteorology, Climatology and Geophysics Agency. Apart from regulations, the results of wave height predictions using the Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) methods can help fishermen determine shipping departures, thereby reducing the risk of accidents. In this study, the Grid Search hyperparameter tuning process was used for both methods which were carried out on four location coordinates. Based on the analysis results, LSTM is superior in predicting wave height for the next 30 days because it can predict wave height at all three locations, with results at the first location (RMSE 0.045; MAE 0.029; MAPE 8.671%), second location (RMSE 0.051; MAE 0.035; MAPE 10.64%), and third location (RMSE 0.044; MAE 0.027; MAPE 7.773%), while XGBoost only has the best value at fourth location (RMSE 0.040; MAE 0.025; MAPE 7.286%).Hyperparameter tuning with gridsearch is used in LSTM and XGBoost to obtain optimal accuracyLSTM outperforms in three locations, while XGBoost outperforms in the fourth location.Advanced prediction techniques such as LSTM and XGBoost improve fishermen's safety by providing accurate wave height estimates, thereby reducing the possibility of shipping accidents.

Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method

Background: The rise in forest fires over the last two years, which is due to rise in dry weather conditions and human activities, have greatly impacted an area of 1.6 million hectares, leading to significant ecological, economic, and health issues, hence the need to improve disaster response strategies. Previous research determined the lack of coverage regarding public response during forest fires with conventional methods such as satellite images and sensor data. However, social media platforms provide real-time information generated by users, along with location information of disaster events. Sentiment analysis helps in understanding the public reactions and responses to natural disasters, thereby increasing awareness about forest fires. Objective: The purpose of this research is to assess the efficiency of Long Short-Term Memory (LSTM) method in classifying sentiment for social networks in regard to forest fires. This research aims to examine the effect of TF-IDF, unigram, and the FastText features on the effectiveness of the classification of sentiment. Methods: The precision, recall, and F1 score of 2, 3, and 4 determined in the LSTM models with commonly available sentiment analysis tools, such as the Vader Sentiment Analysis and SentiWordNet was used to evaluate the performance of the model. Results: With an improvement of roughly 10%, the four layers of the LSTM model generated the best performance for the evaluation of sentiments about forest fires. The LSTM model with FastText achieved F1, recall and precision scores of 0.649, 0.641, and 0.659, which exceeds lexicon-based method including SentiWordNet and Vader. Conclusion: The experimental results showed that the LSTM model outperformed lexicon-based methods when used to analyse the tweets related to forest fire. Additional research is required to integrate rule-based models and LSTM models to develop a more robust model for dynamic data. Keywords: Forest Fire, Disaster, Long Short-Term Memory, LSTM, Vader, SentiWordnet

Monitoring terrestrial water storage changes using GNSS vertical coordinate time series in Amazon River basin

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.

An Improved Rolling Bearing Fault Diagnosis Model of Long Short-Term Memory Network Based on VMD Denoised Vibration Signals

Data driven fault diagnosis methods are increasingly being used for condition monitoring of rotating machinery in the era of Industry 4.0. The effectiveness of Variational Mode Decomposition (VMD) denoised vibration signals in improving the Long Short-Term Memory (LSTM) method for the intelligent fault diagnosis of a rolling bearing is reported. The raw and VMD denoised vibration signals of rolling bearings are provided as inputs to the LSTM network for classification of bearing condition. The efficacy of the methodology to extract the fault information is assessed through datasets obtained from experiment test rig and through the open-source dataset of Case Western Reserve University (CWRU). A comparative analysis is also carried out using both VMD based decomposition and denoising techniques along with four machine learning classifiers viz. Decision Tree, k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) by using the statistical features of the VMD modes. Among the different methods evaluated, VMD denoised signals when fed to LSTM result in the maximum classification accuracy of 99.14 % with experimental dataset. For the case of CWRU dataset, VMD denoised signals as input to the SVM resulted in maximum classification accuracy of 98.16%.

Improved RNN Model for Real-Time Human Activity Recognition

This study focuses on the automatic detection of human actions in video streams. The requirement to detect what human activities happen in videos is recognition of human action due to significant differences in people's visual and motion appearance and actions, camera perspective shifts, moving background, occlusions, noise, and a massive amount of video data. The human activity recognition challenge involves identifying physical activities carried out by individuals or groups based on traces of movements, including gestures, actions, interactions, and group activities. The detection of concepts usually requires additional annotations for the training dataset. In this paper, useful methods for categorizing human action recognition are discussed. The current models are an accurate deep learning method that is based on models that have been changed to be more useful. The large disparities that result from the backdrop and the size of the objects have prevented the identification of activities in videos from being fully and effectively addressed. The main objective is to achieve better accuracy for the Long Short-Term Memory (LSTM) method, which was used to improve the Recurrent Neural Networks (RNN) model. In this paper, LSTM is used to come up with models for different action recognition tasks. The model was made better by making the LSTM have four layers and putting 128 units, 64 units, 32 units, and 16 units in each layer, respectively. In addition, the performance evaluation of deep learning-based approaches has been compared to other related works. Therefore, an improved approach to RNN is proposed to recognize human actions. To classify the videos, a multilayer RNN with a specific type of LSTM is used to extract features from video sequences. The UCF-101 and UCF Sports human action recognition datasets are utilized in this study for both training and assessment. Test findings demonstrate that the suggested strategy achieved increased accuracy. Finally, the enhanced RNN model's total model accuracy in the UCF-101 dataset is 93.78% and 95.70% for the UCF Sport dataset.

Optimizing biomass energy production in the southern region of Iran: A deterministic MCDM and machine learning approach in GIS

This study employs a deterministic approach, distinguishing itself from other renewable energy evaluations, to assess the potential of electrical energy derived from biomass sources in the southern region of Iran. The primary objectives include pinpointing optimal locations for maximal biomass production and subsequent energy generation within distinct climates and topographies, using fuzzy- Analytic Hierarchy Process (AHP). Additionally, Principal Component Analysis (PCA) identify key factors influencing biomass and energy production. The study quantifies electrical and thermal energy derived from biomass sources across various climates. The findings indicate that regions with lower altitudes and humid climates (1530 km2) demonstrate superior biomass performance, leading to increased electrical and thermal energy production. The feature selection process highlights the significant impact of climate and soil characteristics on biomass production and energy output. Analysis of biomass energy production reveals maximum electrical energy production ranging from 674.88 kWh/ha to 711.36 kWh/ha. The results of the Long Short-Term Memory (LSTM) method confirm its high accuracy in estimating electrical energy, with a significant correlation coefficient of 0.98. We conclude that by identifying locations with the best biomass sources based on climate, it is possible to increase the derived electrical energy. These insights are critical for informing energy policies aimed at optimizing biomass energy production and its integration into sustainable power grids.

Data-driven void growth prediction of aluminum under monotonic tension using deep learning

Void growth plays a significant role in ductile fracture prediction of aluminum. This study proposes 2 deep learning models to address this issue. For voids that retain their ellipsoidal characteristics during growth, the ellipsoidal void Semiaxes Long Short-Term Memory (SLSTM) method is proposed, using the 3 principal features of the ellipsoid to represent the voids. For voids that undergo arbitrary shape changes during growth, an innovative deep learning method called Voronoi tessellation-assisted LSTM (VLSTM) is proposed. This method uses the Voronoi algorithm to standardize data features and employs Principal Component Analysis (PCA) to perform data compression before neural network training. This new method combines the Voronoi algorithm, LSTM neural networks, and PCA algorithms, and is termed as VLSTM-PCA. In this study the deep learning-based SLSTM surrogate models and VLSTM-PCA surrogate models run approximately 514 and 537 times faster than ABAQUS finite element simulations, significantly enhancing efficiency while maintaining high prediction accuracy. Finally, growth patterns of ellipsoidal voids under different stress triaxialities are analyzed.

Spatiotemporal prediction of surface roughness evolution of C/C composites based on recurrent neural network

Carbon/carbon (C/C) composites are widely used in aerospace applications due to their excellent properties. Their surface roughness is an important factor affecting the material properties and ablation resistance, which is essential for the accurate prediction of the material. Images are often used to summarize and characterize microstructural features, which include information such as fiber microstructure and fiber orientations. An ablation shape evolution model based on optimization of attention mechanism, which combines multi-scale convolutional neural network (MSCNN) and long short-term memory (LSTM) methods for the C/C composites, is proposed. MSCNN encodes the microstructural information of the ablation image, which is fed into the LSTM model to extract the spatiotemporal features and long-term dependencies latent in the temporal law. The attention mechanism is used to assign different weights to the extracted features which makes the model focus more on the important time steps in the sequence. The results show that the proposed model achieves excellent effects in predicting the evolution of ablative morphology, with high agreement with the simulated values. The method saves time and cost, while providing an efficient and reliable method for the design and optimization of thermal protection materials.

Temperature history reconstruction in steel box girders using limited data and proper orthogonal decomposition-based dimension reduction representation

The monitoring temperature history of steel box girders inevitably contains gaps or anomalies due to the malfunctions of the structural health monitoring system. Traditional methods for reconstructing temperature histories face the challenge in accounting for the randomness of temperature variations caused by the uncertainty of complex environmental factors. This research proposes a framework for reconstructing the long-term temperature of steel box girders by combining the limited measurements with a proper orthogonal decomposition (POD) based dimension reduction representation approach, to account for the stochastic nature of daily temperature variations. Unlike the conventional Monte Carlo-based POD method, the developed POD-based dimension reduction representation approach effectively reduces the number of elementary random variables from thousands to two by introducing random functions serving as constraints, overcoming the challenge of high-dimensional random variables inherent in the Monte Carlo methods. The proposed approach divides the measured temperature histories into random high-frequency (HF) and deterministic low-frequency (LF) components and establishes the theoretical models of power spectral density and coherence functions of HF temperature components to accommodate the generation of HF temperature component samples, and finally reconstructs temperature samples by superimposing the LF components and generated HF component samples. The results from a practical example demonstrate that the statistical characteristics of representative HF temperature component samples generated by the POD-based dimension reduction representation align well with the corresponding targeted values, and the proposed method outperforms the traditional POD method, yielding a 60% efficiency enhancement without compromising computational accuracy. The developed framework owns apparent superiority in accuracy compared to the traditional POD and the long short-term memory methods, particularly in continuous and extensive missing data. Moreover, the reconstructed temperature samples with assigned probabilities present complete probability information from the level of total probability. These results advance the probabilistic methods in tackling long-term temperature history reconstruction.

Forecasting and unveiling the impeded factors of total export of Bangladesh using nonlinear autoregressive distributed lag and machine learning algorithms

Rising global oil prices are a major challenge for an emerging oil-importing nation such as Bangladesh. The majority of prior research on the economic effects of an oil price shock has concentrated on developed countries, with emerging economies receiving comparatively less attention. Bangladesh is vulnerable to price shocks due to its rising oil consumption over the past decade. This study aims to investigate how changes in oil prices would affect Bangladesh's total export earnings and to forecast the overall export volume. This study utilized a nonlinear autoregressive distributed lag (NARDL) approach to account for the asymmetric behavior of oil prices from 1991 to 2021. To assess the accuracy of predictions, the study employed the Prophet forecasting model and the Long Short-Term Memory (LSTM) method. Additionally, the symmetry test revealed a nonlinear relationship between export volume and oil price but a linear relationship between inflation and export volume. According to the NARDL assessment, both positive and negative oil shocks increase export earnings over the long run. The short run summary clarifies that both positive and negative changes in oil prices exert a significant negative effect on exports. Also, Inflation influences export earnings negatively in the short run but positively over the long term. Moreover, using machine learning methods, it was found that the LSTM method outperforms the prophet model in prediction performance with a low root mean square error (RMSE) of 1.88. Also, the analysis revealed policymakers that the export sector requires diversification to reduce its exposure to oil price shocks.

Comparison of Linear Regression and LSTM (Long Short-Term Memory) in Cryptocurrency Prediction

Abstract Cryptocurrency, particularly Bitcoin, has become a major topic in the financial and digital trading sectors due to its ability to facilitate direct transactions without intermediaries and the transparency offered by blockchain technology. However, the high volatility of Bitcoin prices necessitates accurate prediction methods to support better investment decisions. This research aims to compare the accuracy of Linear Regression and Long Short-Term Memory (LSTM) methods in predicting Bitcoin prices using historical data from Yahoo Finance. The research process begins with the collection of historical Bitcoin price data from September 17, 2014, to July 15, 2024, followed by data processing that includes cleaning and splitting the dataset into training and test data. Linear Regression and LSTM models are applied to the training data and tested to evaluate their performance in price prediction. The research findings show that the LSTM model significantly outperforms the Linear Regression model in terms of prediction accuracy. The LSTM model records much lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), as well as perfect R² scores on both datasets, demonstrating its high precision in prediction. In contrast, the Linear Regression model shows higher errors and lower explanatory power of data variability. These findings indicate that LSTM is more effective in capturing temporal patterns and Bitcoin price fluctuations, offering better accuracy and potentially being more suitable for future cryptocurrency price analysis, providing better guidance for investors in this highly dynamic market.

Combining signal decomposition and deep learning model to predict noisy runoff coefficient

Predicting runoff coefficient (Rc), as an indicator of the catchment’s response to the rainfall-runoff process, remains a persistent challenge using different modelling techniques, especially in catchments with strong human manipulation. This study investigates the efficiency of the Long Short-Term Memory (LSTM) method in predicting Rc for the Rur catchment, in Germany. The period from 1961 to 2021 is considered, which is subject to human intervention and significant urbanization especially in the northern part of the catchment. An LSTM structure is defined by employing inputs at a monthly resolution including temperature, precipitation, soil water storage, and total evaporation with a look-back window of 1 to 6 months to model noisy Rc data of the study area. Two approaches using either undecomposed or decomposed Rc were employed in conjunction with the LSTM method, to mitigate the impact of noise associated with Rc. The results show that in the case of undecomposed Rc, the best performance of the LSTM structure was obtained with a 4-month look-back window, yielding Nash-Sutcliffe efficiency (NSE) of 0.55, 0.46, and 0.15 for training, validation, and test sets, respectively. These results highlight inadequate accuracy in accounting for the presence of noise in Rc. Therefore, in the second novel approach, we used maximal overlap discrete wavelet transform (MODWT) to decompose the Rc up to level 3 to reduce the complexity and distribute the noise effects across each level. The new approach showed high accuracy in modelling noisy data of Rc with NSE values of 0.97, 0.95, and 0.90 for training, validation, and test sets, respectively. The obtained results underscore the pivotal role of decomposition techniques in conjunction with LSTM to account for the presence of noise, especially in catchments with strong human manipulation.

An efficient method of predicting S-wave velocity using sparse Gaussian process regression for a tight sandstone reservoir

The shear wave (S-wave) velocity plays a crucial role in interpreting the lithology in seismic data, identifying fluids and predicting reservoirs. However, S-wave velocity is often unavailable due to the high cost of measurement and technical constraints. Conventional methods exhibit limitations that potentially impact the accuracy or efficiency on predicting S-wave velocity. Moreover, these methods always ignore the uncertainty quantification associated with the predicted results. This paper proposes a sparse Gaussian process regression (SGPR) method to predict the S-wave velocity in tight sandstone reservoirs. SGPR is a highly efficient regression technique that is based on the Gaussian process regression (GPR) method. In the SGPR method, inducing inputs are introduced to approximate the kernel matrix to decrease the computational complexity. A sparse set of inducing inputs and kernel hyperparameters are optimized through minimizing the Kullback-Leibler (KL) divergence between the exact posterior distribution and the approximate one. In this study, we select several types of logging data, which include porosity, water saturation, shale content, lithology and P-wave velocity, as the inputs for the SGPR method to predict S-wave velocity. To validate its effectiveness, we use the SGPR method to predict S-wave velocity in tight sandstone and compare the results with those from the GPR method, the bidirectional long short-term memory (BiLSTM) method and the Xu-White model. Additionally, we conduct cross-validation to demonstrate the robustness of the SGPR method. Our findings indicate that the SGPR method presents better performance and significant advantages about the accuracy and efficiency. Moreover, the SGPR method offers uncertainty quantification for the predicted S-wave velocity.

Recognizing salat activity using deep learning models via smartwatch sensors

In this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for Praying), which includes eight different activities: walking, running, sitting, standing, walking upstairs, walking downstairs, typing with a keyboard, and praying (salat). The HAR-P dataset was collected from 50 male individuals, who wore smartwatches on their dominant wrists. We compare the activity classification performance using three state-of-the-art algorithms from the literature: Long Short-Term Memory, Convolutional Long Short-Term Memory, and Convolutional Neural Network—Long Short-Term Memory. To assess the influence of sensors, data from accelerometer, gyroscope, linear acceleration sensor, and magnetic field sensor were utilized. The impact of individual sensor data as well as combinations thereof was investigated. The highest classification accuracy within single sensor groups, reaching 95.7%, was achieved using the accelerometer data with the Convolutional Long Short-Term Memory method. Combining two sensor groups resulted in an increase in accuracy of up to 9%. The highest accuracy of 96.4% was obtained by utilizing three sensor groups together with the Convolutional Neural Network—Long Short-Term Memory method. Furthermore, the evaluation of sensor and model performance was conducted using the stratified k-fold cross-validation method with 5-folds. These findings contribute significantly to evaluating the performance of sensor combinations and different algorithms in activity classification. This study may provide an effective foundation for the automatic recognition and tracking of human activities and offer an applicable model, particularly for the recognition of religious practices such as praying.

A deep learning-based convolutional spatiotemporal network proxy model for reservoir production prediction

Accurate production prediction is crucial in the field of reservoir management and production optimization. Traditional models often struggle with the complexities of nonlinear relationships and high-dimensional data, which hinders their ability to capture the variability of the production process efficiently and results in time-consuming calculations. To overcome these limitations, this paper introduces an innovative proxy modeling technique employing a convolutional spatiotemporal neural network. This method utilizes convolutional neural networks to extract spatial features from high-dimensional data, while the Transformer is used to model and predict complex temporal dynamics in production. To validate the effectiveness of the proposed proxy model, two case studies involving four injection and nine production wells within two-dimensional (2D) and three-dimensional (3D) non-homogeneous reservoirs were conducted, with the R2 coefficient serving as the primary evaluation metric. As the number of training iterations and data volume increase, the proxy model demonstrates rapid convergence. In tests conducted on the 2D and 3D datasets, the average R2 value exceeded 0.96 and 0.94. These results confirm the accuracy and stability of the proxy model. It also shows that the proxy model can accurately describe the geological and fluid seepage characteristics of the reservoir, which in turn can achieve a highly accurate match with the real data. In addition, the computational time is reduced by two orders of magnitude compared to traditional models. Compared with the long short-term memory method, the accuracy of the prediction results is increased by 30%, which greatly enhances efficiency and accuracy. To some extent, the presented proxy model can provide some guidance for the efficient history match of production data.

A hybrid forecasting method considering the long-term dependence of day-ahead electricity price series

Day-ahead electricity market is crucial for ensuring the balance of electricity supply and demand. Its electricity price serves as a key guide for market participants and power dispatching agencies, making accurate short-term price forecasting essential. In this paper, a hybrid short-term electricity price forecasting method considering the long-term dependence of wavelet transform (WT) series is proposed. Different from the existing studies that do not distinguish the characteristics of WT series, this paper uses Hurst exponent and autocorrelation function (ACF) and partial autocorrelation function (PACF) plots to analyze the long-term dependence of subseries, and chooses seasonal autoregressive integrated moving average (SARIMA) or long short-term memory (LSTM) method to construct a hybrid forecasting model according to the characteristics of each subseries. Validation using real electricity prices from the NYISO and PJM markets shows that, compared to methods that do not consider long-term dependencies of subseries, the proposed method can improve prediction accuracy by up to 50.98 % and stability by up to 68.09 %.

Hybrid deep learning-based traffic congestion control in IoT environment using enhanced arithmetic optimization technique

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods.