Types Of Prediction Models Research Articles

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.

Semantic-aware aircraft trajectory prediction using flight plans

Aircraft trajectory prediction (TP) is a challenging and inherently data-driven time-series modeling problem. Adding annotation or enrichment parameters further increases the search space complexity, especially when ‘blind’ optimization algorithms are employed. In this paper, flight plans, localized weather and aircraft properties are introduced as trajectory annotations that enable modeling in a space higher than the typical 4-D spatio-temporal. A multi-stage hybrid approach is employed for a new variation of the core TP task, the so-called Future Semantic Trajectory Prediction, including clustering the enriched trajectory data using a semantic-aware similarity function as distance metric. Subsequently, a separate predictive model is trained for each cluster, using a nonuniform graph-based grid that is formed by the waypoints of each flight plan. In practice, flight plans constitute a constrained-based training of each predictive model, one for each waypoint, independently. The proposed method is formulated and experimentally validated with real aviation dataset (flight plans and IFS radar tracks) and localized weather data for a 1-month time frame of flights in the Spanish airspace. Various types of predictive models are tested, including hidden Markov model (HMM), linear regressors, regression trees and feed-forward neural networks. The results show very narrow confidence intervals for the per-waypoint TP errors in HMM, while the more efficient linear and nonlinear regressors exhibit 3-D spatial accuracy much lower than the current state of the art, up to a factor of five compared to ‘blind’ TP for complete flights, in the order of 2–3 km compared to the actual flight routes.

Multiple mechanical properties prediction of hydraulic concrete in the form of combined damming by experimental data mining

Abstract The application of combined damming with large volume conventional concrete and roller compacted concrete indicates that the development of hydraulic concrete materials embarking on a new stage. However, the large particle size of coarse aggregate and high fly ash content in hydraulic concrete lead to a highly nonlinear relationship between mixture proportion and mechanical properties. This phenomenon will increase the difficulty, workload, and costs of mechanical experiments. To solve these problems, data mining techniques are mostly applied to the prediction of single concrete performance but not multiple mechanical properties. This research compares four data mining models in predicting the mechanical properties of hydraulic concrete in the form of combined damming. These models involve a linear regression model (Bayesian Ridge), an advanced predictive model (Gaussian Processes), a regression tree model (Decision Trees) and an ensemble learning regression model (Gradient Boosting). The performance measures of these techniques are evaluated on the basis of the concrete data from the combined damming engineering. The results show that ensemble regression model (Gradient Boosting) performs higher accuracy, better measures, and stronger robustness than the other three types of prediction models used to predict the mechanical properties of hydraulic concrete. The feature importance of concrete components derived from Gradient Boosting is analyzed and validated by conclusions of previous studies. Therefore, efficient prediction of mechanical properties and rapid mixture proportion designs of hydraulic concrete can be realized and the construction technology of combined damming will be applied widely.

An integrated recommender system for improved accuracy and aggregate diversity

Information explosion creates dilemma in finding preferred products from the digital marketplaces. Thus, it is challenging for online companies to develop an efficient recommender system for large portfolio of products. The aim of this research is to develop an integrated recommender system model for online companies, with the ability of providing personalized services to their customers. The K-nearest neighbors (KNN) algorithm uses similarity matrices for performing the recommendation system; however, multiple drawbacks associated with the conventional KNN algorithm have been identified. Thus, an algorithm considering weight metric is used to select only significant nearest neighbors (SNN). Using secondary dataset on MovieLens and combining four types of prediction models, the study develops an integrated recommender system model to identify SNN and predict accurate personalized recommendations at lower computation cost. A timestamp used in the integrated model improves the performance of the personalized recommender system. The research contributes to behavioral analytics and recommender system literature by providing an integrated decision-making model for improved accuracy and aggregate diversity. The proposed prediction model helps to improve the profitability of online companies by selling diverse and preferred portfolio of products to their customers.

A Predictive Model for Damage Assessment and Deformation in Blast Walls Resulted by Hydrocarbon Explosions

In this paper, a new method is developed to find the ductility ratio in blast walls, resulted by hydrocarbon explosions. In this method, only the explosion energy and distance from the centre of explosion are required to find the damage by using simple predictive models in terms of empirical‐type formulas. The explosion model herein is a TNO multiphysic method. This provides the maximum overpressure and pulse duration in terms of the explosion length and distance from explosion centre. Thereafter, the obtained results are combined with the SDOF model of the blast wall to determine the ductility ratio and the damage. By using advanced optimisation techniques, two types of predictive models are found. In the first model, the formula is found in terms of 2 parameters of explosion length and distance from explosion centre. However, the 2nd model has 3 parameters of explosion length, distance, and also the natural period of the blast wall. These predictive models are then used to find explosion damages and ductility ratio. The results are compared with FEM analysis and pressure‐impulse (P‐I) method. It is shown that both types of models fit well with the outputs of the simulation. Moreover, results of both models are close to FEM analysis. The comparison tables provided in this paper show that, in the asymptotic region of P‐I diagrams, results are not accurate. Therefore, this new method is superior to classical pressure‐impulse (P‐I) diagrams in the literature. Advantage of the new method is the easy damage assessment by using simple empirical‐type formulas. Therefore, the researchers can use the method in this paper, for damage assessment in other types of blast resistive structures.

Model Discrepancy Quantification in Simulation-Based Design of Dynamical Systems

Discrete-time state-space models have been extensively used in simulation-based design of dynamical systems. These prediction models may not accurately represent the true physics of a dynamical system due to potentially flawed understanding of the system, missing physics, and/or numerical approximations. To improve the validity of these models at new design locations, this paper proposes a novel dynamic model discrepancy quantification (DMDQ) framework. Time-instantaneous prediction models are constructed for the model discrepancies of “hidden” state variables, and are used to correct the discrete-time prediction models at each time-step. For discrete-time models, the hidden state variables and their discrepancies are coupled over two adjacent time steps. Also, the state variables cannot be directly measured. These factors complicate the construction of the model discrepancy prediction models. The proposed DMDQ framework overcomes these challenges by proposing two discrepancy modeling approaches: an estimation-modeling approach and a modeling-estimation approach. The former first estimates the model discrepancy and then builds a nonparametric prediction model of the model discrepancy; the latter builds a parametric prediction model of the model discrepancy first and then estimates the parameters of the prediction model. A subsampling method is developed to reduce the computational effort in building the two types of prediction models. A mathematical example and an electrical circuit dynamical system demonstrate the effectiveness of the proposed DMDQ framework and highlight the advantages and disadvantages of the proposed approaches.

296 Can Machine Learning Help to Predict Delays in Acute Stroke Care?

Time to revascularization is a key factor affecting the outcome of acute stroke care. We aimed to assess the utility of machine learning with a neural network for predicting the Door to Needle time (DNT) in acute stroke care. This intervention would help alert the treatment team of potential delays associated with the arrival of new patients with an acute cerebrovascular accident (CVA). Historical patient medical records of acute stroke thrombolysis available at our hospital from 2003-2018 (15 years) were used. Data from 2003-2015, consisting of 221 cases, were used in training. Data from 2016-2018 were used for validation. A 3-layered fully connected neural network was implemented using the Tensor Flow framework (An open source machine learning framework). Duration, severity by National institute of health stroke scale (NIHSS), age in years and hour of day at arrival were identified as the predictive features. A door to needle time of above 45 minutes was set as the target for training the neural network. The network after training showed a positive predictive value of 63% and sensitivity 67% on validation, with an overall accuracy of 60%, defined as the percentage of delay > 45 minutes, which was correctly predicted. With the above 4 parameters, though individually having a weak association, it was possible to develop a prediction model for DNT with 60% accuracy and 63% positive predictive value. This type of prediction models can be a potential solution to optimize and help identify errors or delays in various acute care pathways including those for acute stroke, acute myocardial infarction, and sepsis. Application of machine learning in this context of optimizing acute care based on a predictive model has not been reported in the literature so far.

Machine Learning Approach to Short-Term Traffic Congestion Prediction in a Connected Environment

Traffic congestion is a complex phenomenon triggered by a combination of multiple interacting factors. One of the main factors is the disturbances caused by individual vehicles, which cannot be identified in aggregate traffic data. Advances in vehicle wireless communications present new opportunities to measure traffic perturbations at the individual vehicle level. The key question is whether it is possible to find the relationship between these perturbations and shockwave formation and utilize this knowledge to improve the identification and prediction of congestion formation. Accordingly, this paper explores the use of three machine learning techniques, logistic regression, random forests, and neural networks, for short-term traffic congestion prediction using vehicle trajectories available through connected vehicles technology. Vehicle trajectories provided by the Next Generation SIMulation (NGSIM) program were utilized in this study. Two types of predictive models were developed in this study: (1) offline models which are calibrated based on historical data and are updated (re-trained) whenever significant changes occur in the system, such as changes/updates to the infrastructure, and (2) online models which are calibrated using historical data and updated regularly using real-time information on prevailing traffic conditions obtained through V2V/V2I communications. Results show that the accuracy of the models built in this study to predict the congested traffic state can reach 97%. The models presented can be used in various potential applications including improving road safety by warning drivers of upcoming traffic slowdowns and improving mobility through integration with traffic control systems.

Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production

The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha−1 and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

A Grey Wolf Optimizer-based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills

The tenacity of spun yarns is related to many process parameters and fiber properties. Different types of predictive models have been developed to predict the spun yarns tensile strength; however, no investigation has yet been carried out on prediction of siro-spun yarns tensile strength. This is due to the fact that the relationship between yarn strength and fiber properties and process parameters is essentially non-linear and therefore the prediction of yarn tenacity is a highly complex issue. This paper proposes a Grey Wolf Optimizer (GWO) based Neural Network Simulator called “GWNN” for prediction of siro-spun yarn tensile strength. In the proposed GWNN, a GWO algorithm is applied as a global search method to determine weights of a Multi-Layer Perception (MLP). Additionally, a new Response Surface Methodology (RSM) is proposed to determine appropriate level of fiber and yarn linear densities and processing parameters on yarn tensile strength. The proposed RSM uses the GWNN model as a non-linear response surface simulator and has higher accuracy than the classical RSM. The prediction accuracy of the GWNN was compared with that of a MLP neural network trained with Back-Propagation (BP) algorithm and a Multiple Linear Regression model as well as three evolutionary-based neural networks. It was found that the proposed GWNN enjoys higher accuracy as compared with other models. Additionally, the observed trends in variation of yarns tensile strength with input variables were discussed with reference to inner structure of yarns.

Monitoring and Prediction of Air Quality using Neural Network

Air Quality monitoring is very important in today s world. There are many harmful pollutants present in the air which are very harmful for human health. Prolonged consumption of such air may lead to severe death and harmful diseases. It is also harmful for crops as well as animals which may damage natural environment. There are several pollutants which are present in the air that decreases the quality of air such as sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter. Neural Network can be used for prediction of population for short term as well as long term using a deep learning technologies. Neural network specify two types of predictive models. the first one is the a temporal which is for short-term forecast of the pollutants in the air for the short coming or nearest days and the second one is a spatial forecast of atmospheric pollution index in any point of city. The artificial neural networks takes initial information and consider the hidden dependencies are used to improve the efficiency and accuracy of the ecology management decisions. In this paper the forecasting of atmospheric air pollution index in industrial cities based on the neural network model has proposed.

Industry-based equity premium forecasts

PurposeThis paper aims to study whether the industry indexes predict the evolution of the broad stock market in the USA.Design/methodology/approachThe study uses industry indexes to predict the equity premium in the USA. It considers several types of predictive models: constant coefficients and constant volatility, drifting coefficients and constant volatility, constant coefficients and stochastic volatility and drifting coefficients and stochastic volatility. The models are estimated through the particle learning algorithm, which is suitable for dealing with the problem that an investor faces in practice, given that it allows the investor to revise the parameters as new information arrives. The individual forecasts are combined based on their past performance.FindingsThe results reveal that models exhibit significant predictive ability. The models with constant volatility exhibit better performance, at the statistical level, but the models with stochastic volatility generate higher gains for a mean–variance investor.Practical implicationsThis study’s findings are valuable not only for finance researchers but also for private investors and mutual fund managers, who can use these forecasts to improve the performance of their portfolios.Originality/valueTo the best of the knowledge of the author, this is the first paper that uses particle learning and combination of forecasts to predict the equity premium in the USA based on industry indexes. The study shows that the models generate valuable forecasts over the long time span that is considered.

A System for Automated Determination of Perioperative Patient Acuity.

The widely used American Society of Anesthesiologists Physical Status (ASA PS) classification is subjective, requires manual clinician review to score, and has limited granularity. Our objective was to develop a system that automatically generates an ASA PS with finer granularity by creating a continuous ASA PS score. Supervised machine learning methods were used to create a model that predicts a patient's ASA PS on a continuous scale using the patient's home medications and comorbidities. Three different types of predictive models were trained: regression models, ordinal models, and classification models. The performance and agreement of each model to anesthesiologists were compared by calculating the mean squared error (MSE), rounded MSE and Cohen's Kappa on a holdout set. To assess model performance on continuous ASA PS, model rankings were compared to two anesthesiologists on a subset of ASA PS 3 case pairs. The random forest regression model achieved the best MSE and rounded MSE. A model consisting of three random forest classifiers (split model) achieved the best Cohen's Kappa. The model's agreement with our anesthesiologists on the ASA PS 3 case pairs yielded fair to moderate Kappa values. The results suggest that the random forest split classification model can predict ASA PS with agreement similar to that of anesthesiologists reported in literature and produce a continuous score in which agreement in accurately judging granularity is fair to moderate.

L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification.

The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image classification problems, conventional tree methods use only a few sparse attributes as the splitting criterion. Consequently, they suffer from several drawbacks in terms of performance and environmental sensitivity. To overcome these limitations, this paper introduces a new tree induction algorithm that classifies images on the basis of local area learning. To train our predictive model, we extract a random local area within the image and use it as a feature for classification. In addition, the self-organizing map, which is a clustering technique, is used for node learning. We also adopt a random sampled optimization technique to search for the optimal node. Finally, each trained node stores the weights that represent the training data and class probabilities. Thus, a recursively trained tree classifies the data hierarchically based on the local similarity at each node. The proposed tree is a type of predictive model that offers benefits in terms of image’s semantic energy conservation compared with conventional tree methods. Consequently, it exhibits improved performance under various conditions, such as noise and illumination changes. Moreover, the proposed algorithm can improve the generalization ability owing to its randomness. In addition, it can be easily applied to ensemble techniques. To evaluate the performance of the proposed algorithm, we perform quantitative and qualitative comparisons with various tree-based methods using four image datasets. The results show that our algorithm not only involves a lower classification error than the conventional methods but also exhibits stable performance even under unfavorable conditions such as noise and illumination changes.

Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Crime rate prediction in the urban environment using social factors

The aim of this study is to compare different approaches to the problem of forecasting the number of crimes in different areas of the city. During this research we studied three types of predictive models: linear regression, logistic regression and gradient boosting. The predictive factors used in these models have been selected using the feature selection techniques. This approach allowed us to increase the accuracy of predictions and to avoid the model’s overfitting. The obtained models were tested on criminal data of the city of Saint-Petersburg. We compared the results of model predictions and determined that gradient boosting is the most appropriate method for the problem of crime rate prediction in certain urban area.

Prediction of Railway Vehicles’ Dynamic Behavior with Machine Learning Algorithms

The dynamic performance of railway vehicles needs to be carefully monitored to ensure their safe operation. Presently a number of systems such as the Vehicle Track Interaction Monitor and the Instrumented Revenue Vehicles, utilize a number of on-board inertial sensors to obtain near-real time information on the dynamic performance of railway vehicles. These systems provide rich data sets that give an indication of the underlying track condition and the corresponding dynamic response. This paper outlines the use of Machine learning to develop dynamic behavior predictive models for railway vehicles from measured data. This study worked on the development of 2 types of predictive models, viz. regression and classification model. The regression model predicted the time series dynamic response amplitude and the classification model classified the track sections based on the response distribution over it. Train speed and parameters estimated from the unsprung mass were used as predictors in the model. After the trial of a number of predictive models the Ensemble Tree Bagger method was found to have highest overall prediction accuracy. These predictive models can be utilized as a decision making tool to determine safe operational limits and prioritize maintenance interventions.

Automatic Classification of Users' Health Information Need Context: Logistic Regression Analysis of Mouse-Click and Eye-Tracker Data.

BackgroundUsers searching for health information on the Internet may be searching for their own health issue, searching for someone else’s health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user.ObjectiveThe aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression.MethodsA total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user’s mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2.ResultsAn analysis of variance (ANOVA) analysis found that users’ browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user’s type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users’ age, education level, and the urgency of their information need.ConclusionsA user’s type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function.

Survey on clinical prediction models for diabetes prediction

Predictive analytics has gained a lot of reputation in the emerging technology Big data. Predictive analytics is an advanced form of analytics. Predictive analytics goes beyond data mining. A huge amount of medical data is available today regarding the disease, their symptoms, reasons for illness, and their effects on health. But this data is not analysed properly to predict or to study a disease. The aim of this paper is to give a detailed version of predictive models from base to state-of-art, describing various types of predictive models, steps to develop a predictive model, their applications in health care in a broader way and particularly in diabetes.

Application of Deterministic Model to Predict Compressive Strength of Locally Occurring 3/8 Gravel Concrete at Different Water Cement Ratio and Curing Age

This paper express a new application of determining compressive strength of concrete different from the conventional ways using experimental analysis through calibrations and development of empirical models techniques. Although previous concepts has been generating results to monitor the compressive strength of concrete, but the purpose of these study is to develop another method of compressive strength different from experimental and empirical solution, thus express the advantage of these application compared to other conventional method in concrete strength development. The application of deterministic model in the development of locally occurring 3/8 gravel aggregate concrete at its natural has definitely generated it advantage; it has been observed that these two different materials selected at different mix proportion has also not been thorough monitored applying this type of modeling techniques. These are to determine its compressive strength using this type of predictive model. These concept were used to predict the compressive strength of locally occurring 3/8 gravel concrete at different water cement ratios and curing age, simulating these model generated predictive values that has expressed the behaviour of strength development from different water cement ratios and curing age, the graphical representations shows there various rates of compressive strength at different mix proportions and age, the strength development from these local materials has been expressed through the developed model, some effects that has generated some decline in compressive strength in some mix proportions were also observed, these influences are from variation of concrete porosity and permeability, these are reflected on the fluctuation of compressive strength at different figures expressed through graphical representation, the study is imperative because the developed model can be applied to monitor the behaviour of strength development of all in one aggregates in natural state including selective material that form other normal granite aggregate concrete, predicting there various compressive strength at different ages including every twenty four hour thus interval of seven days to ninety days, these are new concept that can be applied to monitor compressive strength of concrete.