Search results for: Accident Prediction Model
7756 Development of Accident Predictive Model for Rural Roadway
Authors: Fajaruddin Mustakim, Motohiro Fujita
Abstract:This paper present the study carried out of accident analysis, black spot study and to develop accident predictive models based on the data collected at rural roadway, Federal Route 50 (F050) Malaysia. The road accident trends and black spot ranking were established on the F050. The development of the accident prediction model will concentrate in Parit Raja area from KM 19 to KM 23. Multiple non-linear regression method was used to relate the discrete accident data with the road and traffic flow explanatory variable. The dependent variable was modeled as the number of crashes namely accident point weighting, however accident point weighting have rarely been account in the road accident prediction Models. The result show that, the existing number of major access points, without traffic light, rise in speed, increasing number of Annual Average Daily Traffic (AADT), growing number of motorcycle and motorcar and reducing the time gap are the potential contributors of increment accident rates on multiple rural roadway.
Keywords: Accident Trends, Black Spot Study, Accident Prediction ModelProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3091
7755 Combining the Deep Neural Network with the K-Means for Traffic Accident Prediction
Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota
Understanding the causes of a road accident and predicting their occurrence is key to prevent deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.
Keywords: Accident risks estimation, artificial neural network, deep learning, K-mean, road safety.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 262
7754 A Comparison of Grey Model and Fuzzy Predictive Model for Time Series
Authors: A. I. Dounis, P. Tiropanis, D. Tseles, G. Nikolaou, G. P. Syrcos
Abstract:The prediction of meteorological parameters at a meteorological station is an interesting and open problem. A firstorder linear dynamic model GM(1,1) is the main component of the grey system theory. The grey model requires only a few previous data points in order to make a real-time forecast. In this paper, we consider the daily average ambient temperature as a time series and the grey model GM(1,1) applied to local prediction (short-term prediction) of the temperature. In the same case study we use a fuzzy predictive model for global prediction. We conclude the paper with a comparison between local and global prediction schemes.
Keywords: Fuzzy predictive model, grey model, local andglobal prediction, meteorological forecasting, time series.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1995
7753 Automatic Flood Prediction Using Rainfall Runoff Model in Moravian-Silesian Region
Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan
Abstract:Rainfall runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15 – May 18 2014). Prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.
Keywords: Flood, HEC-HMS, Prediction, Rainfall – Runoff.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1910
7752 Using TRACE and SNAP Codes to Establish the Model of Maanshan PWR for SBO Accident
Authors: B. R. Shen, J. R. Wang, J. H. Yang, S. W. Chen, C. Shih, Y. Chiang, Y. F. Chang, Y. H. Huang
In this research, TRACE code with the interface code-SNAP was used to simulate and analyze the SBO (station blackout) accident which occurred in Maanshan PWR (pressurized water reactor) nuclear power plant (NPP). There are four main steps in this research. First, the SBO accident data of Maanshan NPP were collected. Second, the TRACE/SNAP model of Maanshan NPP was established by using these data. Third, this TRACE/SNAP model was used to perform the simulation and analysis of SBO accident. Finally, the simulation and analysis of SBO with mitigation equipments was performed. The analysis results of TRACE are consistent with the data of Maanshan NPP. The mitigation equipments of Maanshan can maintain the safety of Maanshan in the SBO according to the TRACE predictions.
Keywords: PWR, TRACE, SBO, Maanshan.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 586
7751 Development of Neural Network Prediction Model of Energy Consumption
Authors: Maryam Jamela Ismail, Rosdiazli Ibrahim, Idris Ismail
Abstract:In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive review on published research on the energy consumption prediction which focuses on structures and the parameters used in developing Neural Network models. This paper is then focused on the parameter selection of the neural network prediction model development for energy consumption and analysis on the result. The most reliable model that gives the most accurate result is proposed for the prediction. The result shows that the proposed neural network energy prediction model is able to demonstrate an adequate performance with least Root Mean Square Error.
Keywords: Energy Prediction, Multilayer Feedforward, Levenberg-Marquardt, Root Mean Square Error (RMSE)Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2429
7750 Diesel Fault Prediction Based on Optimized Gray Neural Network
Authors: Han Bing, Yin Zhenjie
In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.
Keywords: Fault prediction, Neural network, GM (1.5), Genetic algorithm, GBPGA.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1130
7749 Two States Mapping Based Neural Network Model for Decreasing of Prediction Residual Error
Authors: Insung Jung, lockjo Koo, Gi-Nam Wang
The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.
Keywords: Neural network, U-healthcare, prediction, timeseries, computer aided prediction.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1845
7748 Building the Reliability Prediction Model of Component-Based Software Architectures
Authors: Pham Thanh Trung, Huynh Quyet Thang
Reliability is one of the most important quality attributes of software. Based on the approach of Reussner and the approach of Cheung, we proposed the reliability prediction model of component-based software architectures. Also, the value of the model is shown through the experimental evaluation on a web server system.
Keywords: component-based architecture, reliability prediction model, software reliability engineering.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1292
7747 An Improved Prediction Model of Ozone Concentration Time Series Based On Chaotic Approach
Authors: N. Z. A. Hamid, M. S. M. Noorani
This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly Ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.
Keywords: Chaotic approach, phase space, Cao method, local linear approximation method.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1629
7746 An Enhanced Artificial Neural Network for Air Temperature Prediction
Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom
The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.
Keywords: Time-series forecasting, weather modeling.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1706
7745 Prediction on Housing Price Based on Deep Learning
Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang
In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.
Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4257
7744 A New Hybrid Model with Passive Congregation for Stock Market Indices Prediction
Authors: Tarek Aboueldahab
In this paper, we propose a new hybrid learning model for stock market indices prediction by adding a passive congregation term to the standard hybrid model comprising Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) operators in training Neural Networks (NN). This new passive congregation term is based on the cooperation between different particles in determining new positions rather than depending on the particles selfish thinking without considering other particles positions, thus it enables PSO to perform both the local and global search instead of only doing the local search. Experiment study carried out on the most famous European stock market indices in both long term and short term prediction shows significantly the influence of the passive congregation term in improving the prediction accuracy compared to standard hybrid model.
Keywords: Global Search, Hybrid Model, Passive Congregation, Stock Market Prediction.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1344
7743 Optimum Neural Network Architecture for Precipitation Prediction of Myanmar
Authors: Khaing Win Mar, Thinn Thu Naing
Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.
Keywords: Precipitation prediction, monthly precipitation, neural network models, Myanmar.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1475
7742 A New Quantile Based Fuzzy Time Series Forecasting Model
Authors: Tahseen A. Jilani, Aqil S. Burney, C. Ardil
Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.
Keywords: Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1885
7741 The Economic Cost of Health and Safety in Work Places: An Approach on the Costs Calculating Model
Authors: Efat Lali Dastjerdi, Hassan Sadeghi Naeini, Hadi Sanjari
One of the important steps in a safety and risk management system is the economical evaluation of occupational accident and diseases costs in order to decrease accidents from reoccurring in the workplace. This study proposed a plausible method for calculating occupational accident costs and illnesses in work place. This method design for cost estimation takes into account both the personnel, organizational level as well as the community level especially intended for an Iranian work place. The research indicates that a using systematic method for calculating costs which also provides risk evaluation can help managers to plan correctly the investment in health and safety measures. Using this method is that not only is it comprehensive, easy and practical and could be applied in practice by a manager within a short period of time but it also shows the importance of accident costs as well as calculates the real cost of an accident and illnesses.
Keywords: ost calculating model, Economics of health and Safety.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2063
7740 Stock Market Prediction by Regression Model with Social Moods
Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome
This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model, where document topics are extracted using LDA. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.
Keywords: Regression model, social mood, stock market prediction, Twitter.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2277
7739 Restriction of Iodine Release under Severe Accident Conditions at NPP MIR.1200
Authors: V. Bezlepkin, A. Frolov, L. Lebedev, E. Kharchenko
Abstract:Iodine radionuclides in accident releases under severe accident conditions at NPP with VVER are the most radiationimportant with a view to population dose generation at the beginning of the accident. To decrease radiation consequences of severe accidents the technical solutions for severe accidents management have been proposed in MIR.1200 project, with consideration of the measures for suppression of volatile iodine forms generation in the containment. Behavior dynamics of different iodine forms in the containment under severe accident conditions has been analyzed for the purpose of these technical solutions justification.
Keywords: Iodine radionuclides, VVER, severe accident.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1765
7738 Using RASCAL Code to Analyze the Postulated UF6 Fire Accident
Authors: J. R. Wang, Y. Chiang, W. S. Hsu, S. H. Chen, J. H. Yang, S. W. Chen, C. Shih, Y. F. Chang, Y. H. Huang, B. R. Shen
In this research, the RASCAL code was used to simulate and analyze the postulated UF6 fire accident which may occur in the Institute of Nuclear Energy Research (INER). There are four main steps in this research. In the first step, the UF6 data of INER were collected. In the second step, the RASCAL analysis methodology and model was established by using these data. Third, this RASCAL model was used to perform the simulation and analysis of the postulated UF6 fire accident. Three cases were simulated and analyzed in this step. Finally, the analysis results of RASCAL were compared with the hazardous levels of the chemicals. According to the compared results of three cases, Case 3 has the maximum danger in human health.
Keywords: RASCAL, UF6, Safety, Hydrogen fluoride.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 675
7737 Empirical Statistical Modeling of Rainfall Prediction over Myanmar
Authors: Wint Thida Zaw, Thinn Thu Naing
Abstract:One of the essential sectors of Myanmar economy is agriculture which is sensitive to climate variation. The most important climatic element which impacts on agriculture sector is rainfall. Thus rainfall prediction becomes an important issue in agriculture country. Multi variables polynomial regression (MPR) provides an effective way to describe complex nonlinear input output relationships so that an outcome variable can be predicted from the other or others. In this paper, the modeling of monthly rainfall prediction over Myanmar is described in detail by applying the polynomial regression equation. The proposed model results are compared to the results produced by multiple linear regression model (MLR). Experiments indicate that the prediction model based on MPR has higher accuracy than using MLR.
Keywords: Polynomial Regression, Rainfall Forecasting, Statistical forecasting.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2497
7736 Calibration Model of %Titratable Acidity (Citric Acid) for Intact Tomato by Transmittance SW-NIR Spectroscopy
Authors: K. Petcharaporn, S. Kumchoo
The acidity (citric acid) is the one of chemical content that can be refer to the internal quality and it’s a maturity index of tomato, The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR) spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomato.
Keywords: Tomato, quality, prediction, transmittance, titratable acidity, citric acid.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2586
7735 Improve Safety Performance of Un-Signalized Intersections in Oman
Authors: Siham G. Farag
The main objective of this paper is to provide a new methodology for road safety assessment in Oman through the development of suitable accident prediction models. GLM technique with Poisson or NBR using SAS package was carried out to develop these models. The paper utilized the accidents data of 31 un-signalized T-intersections during three years. Five goodness-of-fit measures were used to assess the overall quality of the developed models. Two types of models were developed separately; the flow-based models including only traffic exposure functions, and the full models containing both exposure functions and other significant geometry and traffic variables. The results show that, traffic exposure functions produced much better fit to the accident data. The most effective geometric variables were major-road mean speed, minor-road 85th percentile speed, major-road lane width, distance to the nearest junction, and right-turn curb radius. The developed models can be used for intersection treatment or upgrading and specify the appropriate design parameters of T-intersections. Finally, the models presented in this thesis reflect the intersection conditions in Oman and could represent the typical conditions in several countries in the middle east area, especially gulf countries.
Keywords: Accidents Prediction Models (APMs), Generalized Linear Model (GLM), T-intersections, Oman.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1831
7734 The Non-Stationary BINARMA(1,1) Process with Poisson Innovations: An Application on Accident Data
Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer
Abstract:This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.
Keywords: Non-stationary, BINARMA(1, 1) model, Poisson Innovations, CMLProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 444
7733 Urban Growth Prediction in Athens, Greece, Using Artificial Neural Networks
Authors: D. Triantakonstantis, D. Stathakis
Urban areas have been expanded throughout the globe. Monitoring and modelling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modelling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.
Keywords: Artificial Neural Networks, CORINE, Urban Atlas, Urban Growth Prediction.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3249
7732 Energy Map Construction using Adaptive Alpha Grey Prediction Model in WSNs
Authors: Surender Kumar Soni, Dhirendra Pratap Singh
Abstract:Wireless Sensor Networks can be used to monitor the physical phenomenon in such areas where human approach is nearly impossible. Hence the limited power supply is the major constraint of the WSNs due to the use of non-rechargeable batteries in sensor nodes. A lot of researches are going on to reduce the energy consumption of sensor nodes. Energy map can be used with clustering, data dissemination and routing techniques to reduce the power consumption of WSNs. Energy map can also be used to know which part of the network is going to fail in near future. In this paper, Energy map is constructed using the prediction based approach. Adaptive alpha GM(1,1) model is used as the prediction model. GM(1,1) is being used worldwide in many applications for predicting future values of time series using some past values due to its high computational efficiency and accuracy.
Keywords: Adaptive Alpha GM(1, 1) Model, Energy Map, Prediction Based Data Reduction, Wireless Sensor NetworksProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1710
7731 A Prediction Method for Large-Size Event Occurrences in the Sandpile Model
Authors: S. Channgam, A. Sae-Tang, T. Termsaithong
In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.
Keywords: Bak-Tang-Wiesenfeld sandpile model, avalanches, cross-correlation, prediction method.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1048
7730 A Type-2 Fuzzy Model for Link Prediction in Social Network
Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi
Abstract:Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.
Keywords: Social Network, link prediction, granular computing, Type-2 fuzzy sets.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1440
7729 A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction
Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju
Abstract:The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.
Keywords: Comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1110
7728 Metabolic Predictive Model for PMV Control Based on Deep Learning
Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon
In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.
Keywords: Deep learning, indoor quality, metabolism, predictive model.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 768
7727 Numerical Prediction of NOX in the Exhaust of a Compression Ignition Engine
Authors: A. A. Pawar, R. R. Kulkarni
Abstract:For numerical prediction of the NOX in the exhaust of a compression ignition engine a model was developed by considering the parameter equivalence ratio. This model was validated by comparing the predicted results of NOX with experimental ones. The ultimate aim of the work was to access the applicability, robustness and performance of the improved NOX model against other NOX models.
Keywords: Biodiesel fueled engine, equivalence ratio, Compression ignition engine, exhausts gas temperature, NOX formation.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1956