Long Short-term Memory Neural Network Model Research Articles

Convolutional LSTM–LSTM model for predicting the daily number of influenza patients in South Korea using satellite images

ObjectivesInfluenza affects a considerable proportion of the global population each year, and meteorological conditions may have a significant impact on its transmission. In this study, we aimed to develop a prediction model for the number of influenza patients at the national level using satellite images and provide a basis for predicting influenza through satellite image data. Study designWe developed an influenza incidence prediction model using satellite images and influenza patient data. MethodsWe collected satellite images and daily influenza patient data from July 2014 to June 2019 and developed a convolutional long short-term memory (LSTM)–LSTM neural network model. The model with the lowest average of mean absolute error (MAE) was selected. ResultsThe final model showed a high correlation between the predicted and actual number of influenza patients, with an average MAE of 5.9010 per million population. The model performed best with a 2-week time sequence. ConclusionsWe developed a national-level prediction model using satellite images to predict influenza incidence. The model offers the advantage of nationwide analysis. These results may reduce the burden of influenza by enabling timely public health interventions.

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

Prediction of air pollutant concentrations based on the long short-term memory neural network

In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R2 reaching over 88%.

Estimating air methane and total hydrocarbon concentrations in Alberta, Canada using machine learning

Fugitive emission sources are significant contributors to methane emissions, and time series data on reported emissions from such sources remain underutilized. The Alberta Energy Regulator (AER) has been collecting air quality data since 1986, including methane and total hydrocarbons concentration data. However, this data has not been thoroughly analyzed to forecast air quality trends. Our analysis of the data shows that average methane concentrations measured at most Alberta airshed stations exceed the global average, and the data exhibits increasing and decreasing trends depending on the station. We compared the predictive performance of three machine learning methods: Long Short-Term Memory (LSTM) recurrent neural network, Fully-Connected Neural Network (FC-NN), and Autoregressive Integrated Moving Average (ARIMA), using the AER methane concentration data. Our results indicate that the LSTM neural network model outperforms the other two methods. Also, our findings suggest that the AER methane concentration data can be effectively analyzed and utilized to forecast air quality trends in the region.

Load Forecasting of Battery Electric Vehicle Charging Station based on GA-Prophet-LSTM

Under the general trend of electric vehicles (EV), aiming at the problem of load forecasting accuracy of electric vehicle charging stations (EVCS), this paper proposes a prediction method combining the Prophet model and Long Short-Term Memory (LSTM) neural network. First, the Prophet model and LSTM neural network model are constructed independently; second, the optimal weight of each model is determined by a genetic algorithm (GA), and a new prediction model is obtained by combining the above two models; finally, experiments are carried out with the charging load data of EV in the real scene. According to the analysis of experimental results, it can be proved that the GA-Prophet-LSTM model presented here is more accurate compared to any single model.

Application of Empirical Mode Decomposition and Hodrick Prescot filter for the prediction single step and multistep significant wave height with LSTM

The study discusses the application of the Empirical Model Decomposition (EMD) and Hodrick Prescot (HP) filter for significant wave height prediction. Long Short-Term Memory (LSTM) is applied for sequence predictions in Natural Language Processing (NLP). LSTM can also be modeled to time series forecasting, particularly for cyclic data since LSTM learns from experience to forecast the data. The time series signal is a combination of frequencies or spectra, when the LSTM is applied to the raw signal the prediction is lagged, to reduce the lag in the forecast the noise in the time series signal is filtered. Since, the time series consists of various frequency components, filtering the higher-frequency components is required to reduce the delay in prediction from the LSTM neural network model. The EMD is applied to the original time series wave data to split the signal into several frequencies in time series or Intrinsic Mode Functions (IMFs). Similarly, when the HP filter is applied to the original wave data or time series signal it gives a trend and cycle. The LSTM is applied to the IMFs and HP filter trend time series to predict the EMD-IMFs and Trend respectively. All the predicted IMFs are summed to compare with the original raw time series. The single step and multistep prediction or forecasting is carried out for 1-h interval, 6-h, 12-h, and 24 h interval. With the increase in forecasting interval the error values also increased and changing the lambda parameter of HP filter played a critical step for the multistep prediction. The EMD-LSTM is computationally expensive as it takes higher amount time to predict each IMF compared to HP-LSTM. Finally, the results are compared with between normal LSTM, EMD-LSTM and HP-LSTM.

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

Time series prediction based on LSTM neural network for top tension response of umbilical cables

The umbilical cable is an essential component of offshore oil and gas extraction systems. The severe marine environment poses a high challenge to the safety of the umbilical cable structure during operation. The analysis of an umbilical cable requires complex and resource-demanding finite element time-domain simulations to obtain their nonlinear dynamic response. Therefore, in order to solve the problem of structural safety monitoring and real-time assessment of remaining life of umbilical cables under extreme sea states, there is a great need to predict the dynamic response of umbilical cables quickly and accurately during operation, for ease of making fast decisions for system operation and maintenance before the arrival of extreme sea states. Given the strong nonlinear function-approximation ability of the neural network, this study proposes an efficient method for the prediction of the time series of umbilical cable top tension response based on LSTM (long short-term memory) neural network. We use LSTM neural network and ARIMA (autoregressive integrated moving average) model in a real engineering case for time series prediction of the top tension response of the umbilical cable, and the results of the two models are analyzed and compared, and the efficiency and accuracy of the LSTM neural network model are verified. Furthermore, the hyperparameter, dataset and generalization ability of LSTM model are discussed. The results indicate that feasibility of the tension response prediction of umbilical cables under dynamic load in complex marine environments.

Neural-network modeling of solar radiation and temperature variability due to climate change in Ibadan Metropolis

his research focused on studying the variability of solar radiation and temperature under climate change in the Ibadan metropolis. In the study, spatial distribution, temporal variations, annual distribution, estimation and prediction of the solar radiation, minimum and maximum temperature data in the Ibadan Metropolis was collected. A Long Short-Term Memory Neural Network (LSTM-NN) model was developed for the prediction using the time-series data obtained. An ARIMA model was further developed to compare and validate the LSTM-NN model. The performance of the prediction models were determined using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The RMSE values for the minimum, maximum temperature and solar radiation predictions were 1.543, 1.290, 1.967, and 1.611, 1.309, 2.106 for the LSTM-NN and ARIMA models respectively, while the MAPE values for the minimum, maximum temperature and solar radiation predictions were 3.603, 4.351, 8.859, and 3.840, 4.480, 9.502 for the LSTM-NN and ARIMA models respectively. The LSTM-NN model had a better prediction performance in all categories with lower error when compared with ARIMA. From the prediction, it was observed that there will be a reduction in the maximum temperature, minimum temperature and solar radiation values when compared to obtained data. The observed minimum temperature ranged from 22.9032-23.2032(0C), while the predicted minimum temperature ranged from 19.9260- 19.977(0C) also the observed maximum temperature ranged from 32.87096-33.7064(0C), while the predicted maximum temperature ranged from 29.5159-29.5529(0C), the observed solar radiation ranged from 19.203-19.722 (W/m2 ), while the predicted solar radiation ranged from 14.123-14.115 (W/m2 ). The year with the highest solar radiation which constitutes the useful energy is 2024 with an average value of 14.1395 W/m2

Electricity supply chain hybrid long‐term demand forecasting approach based on deep learning—a case study of basic metals industry

AbstractDemand forecasting is a key parameter to achieve optimal supply chain management at different levels. The basic metals industry is one of the most energy‐intensive industries in the electricity supply chain. The impossibility of large‐scale energy storage, reservation constraints, and its high costs, limitations on electricity transmission lines capacity, real‐time response to high priority strategic demands, and variety of energy rates at different hours and seasons are issues that challenge the electricity supply chain. A hybrid approach is presented in this paper to improve the accuracy of long‐term demand forecasting in the electricity supply chain. The proposed approach uses wavelet decomposition and long short‐term memory (LSTM) neural networks to produce new predictors for demand forecasting in case of defective data. The data used in this study consists of the recorded hourly demand of Espidan Iron Stone (EIS) company and data of Mobarakeh Steel (MS) company in Isfahan Province. The understudy time series includes a lot of interruptions due to non‐production of the factory or power outages and spikes which cause uncertainty that makes the forecasting more difficult in comparison with continuous time series. Multiple machine learning models including decision trees (DTs), boosted and bagged trees (BGTs), support vector regression (SVR) models, extreme learning machines (ELMs), and a LSTM neural network model are employed to evaluate the effectiveness of the proposed approach in this paper. A comparison of the results obtained using the hybrid proposed approach and training machine learning (ML) methods shows a notable reduction in forecasting error.

Fault Diagnosis of Nuclear Power Plant Based on Sparrow Search Algorithm Optimized CNN-LSTM Neural Network

Nuclear power is a type of clean and green energy; however, there is a risk of radioactive material leakage when accidents occur. When radioactive material leaks from nuclear power plants, it has a great impact on the environment and personnel safety. In order to enhance the safety of nuclear power plants and support the operator’s decisions under accidental circumstances, this paper proposes a fault diagnosis method for nuclear power plants based on the sparrow search algorithm (SSA) optimized by the CNN-LSTM network. Firstly, the convolutional neural network (CNN) was used to extract features from the data before they were then combined with the long short-term memory (LSTM) neural network to process time series data and form a CNN-LSTM model. Some of the parameters in the LSTM neural network need to be manually tuned based on experience, and the settings of these parameters have a great impact on the overall model results. Therefore, this paper selected the sparrow search algorithm with a strong search capability and fast convergence to automatically search for the hand-tuned parameters in the CNN-LSTM model, and finally obtain the SSA-CNN-LSTM model. This model can classify the types of accidents that occur in nuclear power plants to reduce the nuclear safety hazards caused by human error. The experimental data are from a personal computer transient analyzer (PCTRAN). The results show that the classification accuracy of the SSA-CNN-LSTM model for the nuclear power plant fault classification problem is as high as 98.24%, which is 4.80% and 3.14% higher compared with the LSTM neural network and CNN-LSTM model, respectively. The superiority of the sparrow search algorithm for optimizing model parameters and the feasibility and accuracy of the SSA-CNN-LSTM model for nuclear power plant fault diagnosis were verified.

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

Utilizing single light-emitting-diode (LED) lamp and silicon solar-cells visible light positioning (VLP) based on angle-of-arrival (AOA) and long-short-term-memory-neural-network (LSTMNN)

We propose and present the first proof-of-concept demonstration of an angle-of-arrival (AOA)-based visible-light-positioning (VLP) system using long-short-term-memory neural-network (LSTMNN) model. Only a single LED lamp and silicon based solar cell Rx are needed. The LSTM cell can manipulate and analyze time-serial sequential data, reducing the influence of time-dependent fluctuation and noises during the AOA data acquisition; hence, enhancing the positioning accuracy. We compare different ML models including the linear regression (LR) and artificial neural network (ANN), as well as different AOA data sequence lengths and additional LSTM layers. The experimental result reveals that the LSTMNN model outperforms the other models under evaluation. When the sequence length of 20 and one LSTM layer are employed, the mean positioning error is 1.78 cm and the 90% of the experimental data has positioning error within ∼2.9 cm. Compared to the LR model and ANN model, the mean positioning errors are reduced by 49% and 40% respectively; and the 90% cumulative distribution function (CDF) values are reduced by 3 cm and 1.3 cm respectively by utilizing the proposed LSTMNN model.

A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction

Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.

Sustainable Reuse Strategies of Enterprise Financial Management Model Following Deep Learning Under Big Data

The study aims to help enterprises to formulate a financial sustainable development strategy. A financial crisis forecast system based on deep learning (DL) is proposed to assist enterprises in checking their financial bills in time, knowing about their financial situations, formulating corresponding strategies, and realizing financially sustainable development. First, the relevant theories of financially sustainable development and DL are reviewed. Second, a long short-term memory (LSTM) neural network model based on DL is implemented and the normal sample data are compared with the unbalanced sample data. Finally, the performance of the model is analyzed according to the experimental results. The experiments show that the performance of the financial crisis forecast system is the best when the time step is T-3. The accuracy rate of the LSTM model is more than 93%, and the highest value of AUC (area under the curve) is 93.67%. The AUC value of the LSTM neural network model is compared with that of the fully connected neural network model and logistic regression model.

Joint forecasting of multi-energy loads for a university based on copula theory and improved LSTM network

In this study, a multi-energy loads forecasting model based on the artificial intelligence method is proposed. Firstly, based on Copula theory, the nonlinear relationship among different load types themselves, as well as other influence factors affecting the load demand such as ambient temperature etc. are analyzed. Following which, the input factors for load forecasting are selected according to the influence degree. Secondly, based on LSTM (Long Short-Term Memory) neural network model and its improved forms (Stack LSTM and Bidirectional LSTM), the multi-type load forecasting model is developed. A university campus is selected to verify the feasibility and validity of the model compared with the actual data. According to the simulation results, the Stack LSTM illustrates better prediction performance and generalization ability compared with other methods. In addition, the prediction accuracy of cooling and heating demands can be highly improved when considering the affection of electrical load. Moreover, the reduction of load time-scale may obviously increase the prediction accuracy.

HUMAN ACTIVITY RECOGNITION USING LSTM MODEL

Detecting the physical body movements of a human is called Human Activity Recognition . HAR is a time series classifying work, means it can be used in different fields, domains and varieties of application. Sensor data are used. The Movements here are often regular activities like walking, jogging, standing and sitting. In general deep learning architecture manages HAR job with a distinct result. Long Short Term Memory (LSTM) neural architecture which is a deep learning model can be designate as a distinct sort of recurrent neural network model this is able to gaining knowledge of long time dependencies in data. The LSTM neural network model comprises a merger of four layers that interact with each other. We have used Long Short Term Memory neural architecture in UCI HAR dataset to identify the type of activity or movement the person is doing. We saw that after the network completes training, optimization and accuracy is found to be 87% to 90% also a graph and confusion matrix has been used to show the losses, accuracies, training iteration, etc.

USING LONG SHORT-TERM MEMORY MODEL FOR CLOUD FOREST VEGETATION GROWTH STATUS PREDICTION – A CASE STUDY IN SHEI-PA NATIONAL PARK

Abstract. Cloud Forests (CFs) are characterized by their persistent foggy environment, in which fog can save two times the amount of precipitation in the dry season and increase water storage by 10% in the rainy season. CFs play an important role in ecosystems as high biodiversity and abundant endemic species live within CFs. However, CFs are sensitive to environmental changes, especially in current global climate warming conditions. Therefore, a typical cloud forest in Taiwan, Shei-Pa National park, was chosen as the study area. Specifically, the Normalized Difference Vegetation Index (NDVI) with meteorological factors including rainfall, average temperature, maximum temperature, and minimum temperature were obtained to assess the overall CFs trend from 2001 to 2017. Moreover, the Long Short-Term Memory neural network model (LSTM) was implemented to predict the future vegetation status. Preliminary results have shown that vegetation condition in Shei-Pa National park was getting better; rainfall, average temperature, and minimum temperature represented an upward trend while maximum temperature showed a downward trend. Furthermore, the LSTM- maximum temperature model displayed the highest prediction power with the MAPE index of 4.84%. The results provide a valuable reference for forest resource conservation and future climate adaptation strategies in Taiwan.

Evaluation of Dynamic Tensions of Single Point Mooring System under Random Waves with Artificial Neural Network

Real-time monitoring of the mooring safety of floating structures is of great significance to their production operations. A deep learning model is proposed here, based on the long short-term memory (LSTM) artificial neural network. Firstly, the numerical simulation is carried out with the single-point mooring system of a Floating Production Storage and Offloading (FPSO) as the training data of LSTM. Then the proposed LSTM is performed. Finally, taking the motion of FPSO which is not encountered by LSTM neural network model as input, we predict the mooring line tension with this model. Here, one FPSO in the South China Sea is taken as a research case, hydrodynamic and mooring models are established, and the network structure and hyper-parameters of the LSTM model are determined. The prediction results of the LSTM under different combinations of wind, wave, and current are compared with the calculation results of AQWA software. The model constructed here can well predict the mooring line tension of different combinations of wind, wave and current.

Reservoir production prediction with optimized artificial neural network and time series approaches

Numerical simulation of oil reservoirs is one of the most commonly used methods for reservoir production prediction, but its accuracy is based on accurate geological modeling and high-quality history matching. Therefore, numerical simulation is time-consuming and costly and requires extensive information. Traditional back-propagation neural networks and their improved algorithms are widely used for production prediction, but they are not suitable for time-series prediction problems. Based on variations in oil production, this study proposes a reservoir production prediction model based on a combined convolutional neural network (CNN) and a long short-term memory (LSTM) neural network model optimized by the particle swarm optimization (PSO) algorithm. First, the model extracts important temporal data features through the upper CNN, which is next imported to the lower LSTM network to further extract correlation features in the time dimension; then, it iteratively optimizes the key hyperparameters in the CNN-LSTM model through the PSO algorithm; finally, it uses the trained model for reservoir prediction. Compared with the training results of the LSTM neural network and CNN model, the PSO–CNN-LSTM model has higher prediction accuracy in time-series production prediction. Our proposed hybrid model is a data-driven method and is based on routinely available production data. Quick and accurate production prediction can lead to better informed operational decisions and optimization of recovery and economics.