Terms Of Prediction Research Articles

Short Term Prediction of Urban Traffic Flow based on Machine Learning Algorithms

With the increasing per capita car ownership worldwide, existing transportation facilities are unable to fully meet people's travel needs. The massive traffic flow can lead to traffic congestion, so predicting traffic flow can play a crucial role in urban traffic management. This study predicts the westbound traffic flow data of the Minnesota Department of Transportation (MN DoT) ATR 301 station on Interstate 94 in the United States for 28 consecutive days using a Long Short-Term Memory Network (LSTM) data prediction model. The Random Forest is utilized to analyze the association between attributes and the historical data has been separated into 1-hour intervals. The highway displays a bimodal pattern on weekdays and a unimodal pattern on holidays, according to the final forecast results. Furthermore, after making the necessary adjustments to the parameters, the simulated trend closely resembles the real value, proving that LSTM can be used to model short-term traffic flow. Accurately predicting urban traffic flow can make the management of urban traffic management systems more comprehensive and provide citizens with higher quality travel planning references.

The CNN-LSTM-attention model for short term prediction of the polar motion

Abstract The accuracy of polar motion (PM) prediction significantly impacts the fields of coordinate frame transformation, satellite orbit determination, and deep space exploration. The present study develops two short term forecasting models based on the EOP 14C04 series. One hybrid approach incorporates convolutional neural networks (CNNs) and long short-term memory networks (LSTM), augmented with an attention mechanism; whereas another baseline model comprises CNN and LSTM. The first model, in contrast to the second model, incorporates an attention mechanism module for a more comprehensive integration of temporal information at each time step. In the initial short-term forecasting experiment, we conducted 360 repeated predictions, and the findings revealed that the parameters suitable for PMX forecasting may not necessarily be applicable to PMY forecasting. In the second experiment, the two models generated a total of 500 forecasts, each encompassing short-term predictions ranging from 1 to 30 d. The experimental results demonstrate that the first model exhibits mean absolute error (MAE) range of 0–7.72 mas for PMX and 0–4.73 mas for PMY, while the second model shows MAE range of 0–7.88 mas for PMX and 0–4.78 mas for PMY. After two exploratory experiments, we discovered the following results: the first model exhibits marginally superior predictive accuracy compared to the second model. Furthermore, this study substantiates the robustness of both models in short-term prediction and affirms the significance of assigning distinct weights to past temporal intervals in forecasting, thereby offering a novel perspective for PM prediction research.

Convolutional Neural Networks accurately predict soil matric potential from soil, weather, and satellite-derived data

Being able to predict soil moisture dynamics offers water managers the possibility to better plan irrigation events and prevent soil moisture deficits from reaching levels that reduce crop production. Machine learning (ML) model predictions can potentially assist farmers in managing irrigation water more efficiently. In this study, we aimed to assess the accuracy of a set of ML models in predicting soil matric potential seven days ahead in gravity-surface irrigated cotton paddocks and evaluate the models’ performance for longer term predictions (14 days). The ML models used past soil moisture, weather, and satellite-derived crop-related data as features for the input parameters. Input data were structured in tuples that were organised following a 20-day ‘window’ approach that ‘slid’ one position forward after each training round. A convolutional neural network (CNN) model outperformed a Long Short-Term Memory, Dense Multilayer Perceptron, and Linear Regression model, the latter of which produced the least accurate predictions. The accuracy of the soil matric potential predictions with the CNN model was stable over time (R2 ≥ 0.92 and root mean square deviation ≤ 7.5 kPa). However, less accurate predictions were obtained for a short period after emergence and at crop senescence. This study demonstrates the feasibility of producing accurate predictions of soil matric potential in cotton fields at 0.20 m soil depth with a CNN model, which can be integrated into irrigation decision support systems.

A Deep Learning PM2.5 Hybrid Prediction Model Based on Clustering–Secondary Decomposition Strategy

Accurate prediction of PM2.5 concentration is important for pollution control, public health, and ecological protection. However, due to the nonlinear nature of PM2.5 data, the accuracy of existing methods suffers and performs poorly in both short-term and long-term predictions. In this study, a deep learning hybrid prediction model based on clustering and quadratic decomposition is proposed. The model utilizes the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the PM2.5 sequences into multiple intrinsic modal function components (IMFs), and clusters and re-fuses the subsequences with similar complexity by permutation entropy (PE) and K-means clustering. For the fused high-frequency sequences, a secondary decomposition is performed using the whale optimization algorithm (WOA) optimized variational modal decomposition (VMD). Finally, the nonlinear and temporal features are captured for prediction using the long- and short-term memory neural network (LSTM). Experiments show that this proposed model exhibits good stability and generalization ability. It does not only make accurate predictions in the short term, but also captures the trends in the long-term prediction. There is a significant performance improvement over the baseline models. Further comparisons with existing models outperform the current state-of-the-art models.

Biparietal diameter vs crown-rump length as standard parameter for late first-trimester pregnancy dating.

To compare the precision of biparietal diameter (BPD) and crown-rump length (CRL) as predictors of gestational age in the human fetus in the late first and early second trimesters, using a population-based approach. We constructed term and gestational-age prediction curves for first-trimester dating, based on 11 041 pregnancies with 12 260 measurements of CRL and/or BPD from a population-based Norwegian clinical database. We used a population-based approach with local linear quantile regression, combined with a time-to-event strategy that compensates for induced births. Term prediction precision was assessed by estimating and comparing the prediction residual curves using a time-to-event analysis. Individual differences in gestational-age predictions from CRL and BPD were assessed using measurements performed on the same fetus on the same day. A sensitivity analysis was performed to evaluate the effect of not distinguishing between non-spontaneous and spontaneous births. CRL and BPD provided almost identical term prediction precision judged from the residual distribution. In about 51% of examinations, the difference in predicted gestational age was 1 day or less; 24% of examinations had a difference of 2 days, 14% had a difference of 3 days, 7% had a difference of 4 days and only 5% of all examinations had a difference of 5 days or more. Incorrectly removing induced births from the analysis, or treating them as spontaneous, would cause a substantial systematic prediction bias of about 2 days. Based on population data, using comparisons at an individual level, our study found that BPD is as precise as CRL when used for first-trimester dating. BPD has advantages from a clinical point of view, since it is technically less challenging and less time-consuming to measure compared with CRL, and can be measured and assessed throughout the entire pregnancy. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model.

This paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behavior. But as a novel way to model epidemics do they produce valid predictions? We presented DCM predictions 12 months ago, which suggested an increase in viral transmission was accompanied by a reduction in pathogenicity. These changes provided plausible reasons why the model underestimated deaths, hospital admissions and acute-post COVID-19 syndrome by 20%. A further 12-month validation exercise could help to assess how useful such predictions are. we compared DCM predictions-made in October 2022-with actual outcomes over the 12-months to October 2023. The model was then used to identify changes in COVID-19 transmissibility and the sociobehavioral responses that may explain discrepancies between predictions and outcomes over this period. The model was then used to predict future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2024, as a prelude to future tests of predictive validity. Unlike the previous predictions-which were an underestimate-the predictions made in October 2022 overestimated incidence, death and admission rates. This overestimation appears to have been caused by reduced infectivity of new variants, less movement of people and a higher persistence of immunity following natural infection and vaccination. despite an expressive (generative) model, with time-dependent epidemiological and sociobehavioral parameters, the model overestimated morbidity and mortality. Effectively, the model failed to accommodate the "law of declining virulence" over a timescale of years. This speaks to a fundamental issue in long-term forecasting: how to model decreases in virulence over a timescale of years? A potential answer may be available in a year when the predictions for 2024-under a model with slowly accumulating T-cell like immunity-can be assessed against actual outcomes.

Solid-liquid phase behaviour of novel kosmotrope/chaotrope binary mixtures involving sarcosine

The concept of deep eutectic solvents (DESs) has been recently applied to osmolytes (kosmotropes or chaotropes), small organic molecules responsible for stabilization of proteins and other biomolecules in various biological systems. In this study the combinations of the kosmotrope sarcosine (Sar) with chaotrope urea (U) or guanidine hydrochloride (G) across various molar ratios was studied experimentally by the differential scanning calorimetry and infrared spectroscopy. Theoretical prediction of thermodynamic interaction terms based on screening of the charge density was used to support interpretation of experimental findings. Phase diagrams were constructed for both Sar-U and Sar-G systems and eutectic behaviour was identified. Neither of the two systems showed depression of the eutectic temperature compared to its ideal solution values, and therefore neither of the systems could be classified as a deep eutectic solvent in the classical sense. The Sar-G system did show at least partial crystallization of its eutectic solution upon cooling. However, the eutectic solution of the Sar-U system remained amorphous at decreased temperatures, and the phenomenon was attributed to be kinetic in nature and originating from strong intermolecular hydrogen bonding interactions of the components. Thus, it was speculated that the role of osmolytes in stabilizing cellular biomolecules might not come from the thermodynamics only.

An improved viscoelastic k- ε -v2-f turbulence model for intermediate and high drag reduction regimes

In this work, an improved anisotropic k-ε-v2-f model based on the finite extensible nonlinear elastic model with the Peterlin approximation for viscoelastic channel flows is proposed. This model is tested using direct numerical simulation (DNS) data for friction Reynolds numbers (Reτ) in the range of 120–1000, friction Wiesenberg numbers (Wiτ) in the range of 25–116, viscosity ratios (β) in the range of 0.6–0.9, and maximum polymer extensibility values (L2) in the range of 900–14 400. The flow characteristics of viscoelastic fluids with various parameters obtained from the new model agree well with existing DNS results. By adding closures for the flow, shear, and transverse components, the incomplete prediction of nonlinear terms from interactions between the fluctuating components of the conformation and velocity gradient tensors is improved. Compared with DNS results, these closures can fully obtain each component and significantly improve the accuracy of the flow direction component in the intermediate and high drag reduction regimes. Furthermore, the model in this paper retains the advantages of an anisotropic model, does not require a damping function, is simple to construct, and is easily extended to a variety of bounded flows.

Study on the prediction of execution progress term considered the effect of climate

Study on the prediction of execution progress term considered the effect of climate

Forecasting of Inland Fish Production: Case of Catla (<em>Catla catla</em>) and Gift Tilapia (<em>Oreochromis niloticus</em>) Production in Udawalawa Reservoir in Sabaragamuwa Province, Sri Lanka

This study focused on assessing and forecasting Catla (Catla catla) and Tilapia (Oreochromis niloticus) production in the Udawalawe reservoir in Sri Lanka using an 11-year time series data from 2010 to 2021. The relevant secondary data were collected from the National Aquaculture Development Authority of Sri Lanka (NAQDA). Unit root tests, Dickey-Fuller tests, and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models and trend analysis were applied for the data analyses. According to the results, stationarity behaviour was observed in both Catla and Tilapia production information series. Hence, SARIMA (2,2,1) (1,0,1)12 is the appropriate empirical model for forecasting Catla production. Furthermore, the maximum average landings of Catla production were observed in the March-June time period. SARIMA (1,0,1) (1,1,1)12 was indicated as the most suitable model to forecast Tilapia production in the reservoir. The maximum average of Tilapia production was also observed in the March-June period. Both the SARIMA model and trend analysis gave approximately equal production information regarding Catla and Tilapia. Thus, these findings showed that the SARIMA model has more accuracy than trend analysis when analysing the time series information for short durations. However, when the duration becomes larger, forecasting is not suitable using the SARIMA model, and trend analysis gives a clear view up to a certain level. Both Catla and Tilapia production exhibited significant changing patterns and multiple seasonalities. Thus, trend analysis gives valuable insights than SARIMA model in long term predictions in case of this study. These findings are highly important for enhancing fish production in this reservoir, as they predict future fish catches under the current reservoir conditions.

Numerical Study of Laminar Flow and Vortex-Induced Vibration on Cylinder Subjects to Free and Forced Oscillation at Low Reynolds Numbers

In this study, we aimed to numerically investigate the 2D laminar flow over a cylindrical body and performed vortex-induced vibration analyses on a circular cylinder of unit radius placed in a channel, with the cylinder assumed to be fixed. The cases of a cylinder under forced oscillation and three different scenarios of a freely oscillating cylinder were analyzed. The fluid domain dynamics were governed by the incompressible Navier–Stokes equations; however, the structural field was described using nonlinear elastodynamic equations. Fluid and solid domains were discretized with the finite volume method (FVM) in space and time. Predictions of hydrodynamic forces, namely lift and drag terms, were determined for each scenario. An increase in the Reynolds number caused an exponential increment in the lift force. In the case of a stabilized flow, the collective decrease in stiffness and damping decreased the maximal drag and lift factors. Furthermore, it was noticed that the lift factor was minimally altered by variations in damping and stiffness in comparison with the change in the drag factor. From these observations, it appears that the lift factor probably correlates with the cylinder’s structure and fluid properties.

Comparison of simulation tools for optimizing borehole heat exchanger field operation

Model predictive control (MPC) is a promising approach for optimizing the performance of borehole heat exchangers (BHEs) in ground-source heat pump systems. The central element of MPC is the forward model that predicts the thermal dynamics in the ground. In this work, we validate the prediction accuracy of four BHE modeling approaches against real-world measurement data across various operational events and timescales. We simulate the fluid temperature leaving a BHE using a fully discretized 3-D numerical model, a resistance–capacitance model, a g-function model, and a hybrid model. The simulated temperatures are compared to measured temperatures using three validation metrics that quantify temperature offset, noise, and accuracy. The main reason for a mismatch between measured and modeled temperatures is a temperature offset of the simulated temperature. To remove this effect, the models were calibrated for their most sensitive parameter, the ground temperature, and their prediction accuracy over 4 years was evaluated. Thereby, model calibration seems to be a viable solution to account for an unknown load history. The results show that the resistance–capacitance model provides decent predictions in the short term and the g-function model in the long term. However, both models are strongly dependent on accurate calibration. The hybrid model provides the most accurate short and long-term predictions and is less dependent on calibration. Still, its integration into optimization syntax poses challenges compared to the other models. Although not yet applied in model predictive control, the hybrid model stands out as a promising choice for optimizing BHE field operations across various timescales.

A Study of Legal Judgment Prediction Based on Deep Learning Multi-Fusion Models—Data from China

Legal judgment prediction implies predicting judgment results based on the case description suitable for judgment predictions in legal terms, crimes, fines, and punishment. This study examines Chinese legal cases, focusing on legal predictions based on deep learning multiple fusion models. It aims to analyze case descriptions and predict legal terms and fines. The use of artificial intelligence models to predict the law can solve the problem of increasing workload of legal institutions and personnel because of the increasing number of cases; and simultaneously, reduce the differences in judgment. This will improve the efficiency and fairness of legal judgments. This study used the BDCI2017 dataset for experiments and applied deep learning algorithms to improve the prediction accuracy. Various models, such as TextCNN, TextRNN, Wide & TextCNN, and TextDenseNet classify cases and fines. Results revealed that TextDenseNet is better than the other model structures in terms of predictive accuracy.

Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs.

Ship pitch prediction method based on LSTMC and multi-head attention

When ships sail in the sea, ship motion is unstable and stochasticity due to the ever-changing weather and marine conditions. Aiming to address the issue of low prediction accuracy in traditional prediction methods, short term predictions of ship pitch at 1 step, 3 steps, 6 steps, and 12 steps in advance are conducted, and the impact of input-output ratios on the prediction model is studied. The seven types of data of the marine environment and ship motion are taken as model inputs, with pitch as the model output. When predicting 1 step in advance, a prediction model based on LSTM and multi-head attention (LMA) is proposed. This model firstly uses LSTM layer to extract the temporal feature of the input sequence, and then uses the multi-head attention layer to assign larger weights to the key information to enhance prediction accuracy. When predicting 3 steps, 6 steps, and 12 steps in advance, as the input-output ratio increases, the length of the input sequence keeps growing. The model needs to sufficiently capture the local spatial characteristic and long-range dependency between multi-variable inputs, a deep prediction model based on Conv2d and LMA model is proposed. The model firstly uses two-dimensional convolution layer to extract local spatial feature of long sequences, and then through the attention layer, assign larger weights to temporal information and spatial information that are conducive to continuous prediction in order to enhance prediction accuracy. Research on the input-output ratio indicates that, as the number of predicted steps increases, the appropriate ratio continuously decreases, but the length of the input sequence keeps increasing. Finally, four evaluation metrics are used to evaluate the proposed algorithms on real ship data, and the prediction stability and accuracy of the LMA model and the LCMA model are also verified.

Landslide displacement prediction model based on multisource monitoring data fusion

This study presents a displacement prediction model that integrating various monitoring data sources to comprehensive landslide monitoring information utilization. The research focuses on the landslide in Lingwan Village, utilizing Ridge Regression (RR) to integrate diverse monitoring data. The fused displacement data undergo decomposition into trend and periodic terms using the Smoothness Priors Approach (SPA). The Autoregressive Integrated Moving Average (ARIMA) model predicts trend and periodic term displacements separately, which are then combined to forecast the overall landslide surface displacement accurately. The RR model demonstrates a strong correlation coefficient of 0.998, indicating high predictive ability. Trend term displacement prediction outperformed periodic term prediction, especially during the rainy season. The RR-SPA-ARIMA model has an average absolute error of 1.73 mm, a mean bias error of −0.08 mm, and a root mean square error of 1.87 mm. The model demonstrates high prediction accuracy.

Grouping and long term prediction of sunspot cycle characteristics-A fuzzy clustering approach

Based on the pattern recognition algorithm called fuzzy c-means clustering, grouping of sunspot cycles has been carried out. It is found that, optimally the sunspot cycles can be divided in to two groups; we name it as Large Group and Small Group. Based on the fuzzy membership values the groups are derived. According to our analysis, cycles 1,5,6,7,12,13,14,15,16 and 24 belongs to the Small class, where as cycles 2,3,4,8,9,10,11,17,18,19,20,21,22, and 23 belongs to the Large class. Based on the features of each group and its fuzzy cluster center, prediction of cycle 25 is also been made. Also on the periodicity of the occurrence of the groups, a new cyclic behaviour has been found for the occurrences of the identical sunspot cycles. According to our study Cycle 25 belongs to small class and further we predict that the future cycle up to cycle 32 may fall in small group.

Research on Short Term Prediction of Electricity Demand in Hubei Province

The balance of electricity supply and demand is the basic condition for promoting the economic development of our country and improving the living standards of the people, and scientific forecasting of electricity demand is the premise of ensuring the balance of electricity supply and demand. Hubei Province is superior in geographical location, and its electricity demand and economic aggregate both rank high in the country, but due to the direct impact of the epidemic, its electricity demand has been affected. This paper uses OLS regression to obtain the linear regression equation of the three indicators of GDP and the proportion of the secondary industry structure before the epidemic from 2005 to 2018, and uses the Gray Forecast Model-assisted scenario analysis method to analyze the electricity consumption of Hubei Province under the influence of multiple factors, and predicts the electricity consumption of Hubei Province from 2019 to 2021 after removing the impact of the epidemic, and makes a short-term forecast of the electricity demand of the province from 2024 to 2029.

Analysis of ARIMA, LightGBM, XGBoost, and LSTM models for stock prediction

As a matter of fact, stock market prediction has always been a popular problem. Many investors and scholars think that its impossible because they believe its random and doesnt have any patterns. However, many studies have found that long term prediction is possible, and that the existence of patterns in stocks makes it able to be predicted. Because of the possibility of predicting the stock market, many studies and investors have thought of new methodologies, ranging from statistical, economical, and others, employing techniques from a variety of practices. One methodology that has recently gained momentum is machine learning, which shows great promise and improvement. This study looks at four prevalent stock market prediction models, which include ARIMA, LightGBM, XGBoost, and LSTMs, explains some research done with them, the problems they have, and future improvements. It finally briefly discusses other methods researchers have used to predict the stock market that werent explained in the paper.

Data-driven RANS closures for improving mean field calculation of separated flows

Reynolds-averaged Navier-Stokes (RANS) simulations have found widespread use in engineering applications, yet their accuracy is compromised, especially in complex flows, due to imprecise closure term estimations. Machine learning advancements have opened new avenues for turbulence modeling by extracting features from high-fidelity data to correct RANS closure terms. This method entails establishing a mapping relationship between the mean flow field and the closure term through a designated algorithm. In this study, the k-ω SST model serves as the correction template. Leveraging a neural network algorithm, we enhance the predictive precision in separated flows by forecasting the desired learning target. We formulate linear terms by approximating the high-fidelity closure (from Direct Numerical Simulation) based on the Boussinesq assumption, while residual errors (referred to as nonlinear terms) are introduced into the momentum equation via an appropriate scaling factor. Utilizing data from periodic hills flows encompassing diverse geometries, we train two neural networks, each possessing comparable structures, to predict the linear and nonlinear terms. These networks incorporate features from the minimal integrity basis and mean flow. Through generalization performance tests, the proposed data-driven model demonstrates effective closure term predictions, mitigating significant overfitting concerns. Furthermore, the propagation of the predicted closure term to the mean velocity field exhibits remarkable alignment with the high-fidelity data, thus affirming the validity of the current framework. In contrast to prior studies, we notably trim down the total count of input features to 12, thereby simplifying the task for neural networks and broadening its applications to more intricate scenarios involving separated flows.