Stochastic Prediction Model Research Articles

A linear model for long‐term dosage of anticoagulant drugs

AbstractWe discuss a stochastic linear model for effect prediction in anticoagulant therapy. The model is the basis for an expert system for correct dosage of anticoagulant drugs.

A mathematical model of wind flow, vehicle wake, and pollutant concentration in urban road microenvironments. part I: model description

A computer model consisting of wind distribution, emission dispersion, modified Gaussian equation, and stochastic prediction models has been developed to identify street canyon and vehicle wake effects on the transport air pollution from urban road microenvironments. In this work, wind flow and their components in the street canyon are simulated and analyzed considering a two-dimensional street canyon flow pattern. Wind parameters obtained from the analysis are then applied to estimate wind speed and vehicle wake turbulence in microenvironments. Turbulent parameters are integrated in Gaussian equations to estimate pollutant concentrations. A stochastic method has also been considered for predicting maximum concentration in road microenvironments. The model takes traffic data, emission rates, meteorological data, vehicle dimensions, and canyon geometry, and outputs pollutant concentration in microenvironments in an uninterrupted traffic flow section in numerical and graphical form. The model has the predictive capability for CO and NO x.

Stochastic models for characterisation and prediction of time series with long-range dependence and fractality

Time series from various fields, such as geophysics, meteorology, hydrology, air pollution, often display long-range dependence and small-scale behaviour (fractality). This paper develops a new class of stochastic models to represent such properties. An efficient estimation procedure is described and tested on two concentration time series collected in an environmental wind tunnel. These time series simulate two different types of odour sources and possess quite different statistical properties that are well described by the new model.

A nonlinear stochastic model of fatigue crack dynamics

This paper presents a nonlinear stochastic model for prediction of fatigue crack damage in metallic materials. The model structure allows estimation of the current damage state and prediction of the remaining service life based on the underlying principle of Gauss-Markov processes without solving the extended Kalman filter equation in the Wiener integral setting or the Kolmogorov forward equation in the Ito integral setting. The model results have been verified with experimentally-generated statistical data of time-dependent fatigue cracks for 2024-T3 and 7075-T6 aluminum alloys.

Development of the microstructure based stochastic life prediction models : Przystupa, M.A. and Vasudevan, A.K. Report AD-A270 453/4/XAB (1993) 4 pp

Development of the microstructure based stochastic life prediction models : Przystupa, M.A. and Vasudevan, A.K. Report AD-A270 453/4/XAB (1993) 4 pp

Development of the microstructure based stochastic life prediction models. Annual report: Pryztupa, M.A., Zhang, J. and Luevano, A.J. AD-A246 447/7/XAB (1992) 42 pp

Development of the microstructure based stochastic life prediction models. Annual report: Pryztupa, M.A., Zhang, J. and Luevano, A.J. AD-A246 447/7/XAB (1992) 42 pp

TIME-SERIES PREDICTION SYSTEM AND AROP MODEL IN TRANSPORTATION DEMAND ANALYSIS

The purpose of this paper is to discuss the state of fluctuation for the transportation demand which varies with time and to develop a time series prediction system and model considering properties of the fluctuation. The traditional models in time series analysis of data have problems such as insufficient consideration on the trend variation, complexity with including the seasonal variation and ambiguous process of model fitting.A general prediction system and a new stochastic prediction model in time series analysis, AROP model, is proposed. Prediction system consists of subsystems to separate stochastic stationary or nonstationary process, to select a prediction model and to evaluate the precision of prediction. AROP model is divided into two models, AROP 1 and AROP 2, corresponding to trend variation.

Stochastic Flood Analysis in a Region of Mixed Generating Processes

In southwestern British Columbia there exists a transition from winter rain generated floods on the west coast to spring snowmelt generated floods on the Interior Plateau. Each process gives rise to distinct flood types characterized by their magnitude, timing and frequency. The region of transition between the two single process-dominated areas displays a progressive mixture of both types. Existing stochastic floods prediction models are applied to the partial duration and annual flood series of seventeen drainage basins. In all cases the fit of observed to predicted data is found to be adequate. The parameters of the stochastic models are clearly indicative of both the differing characteristics of each generating process and the smooth geographic transition from one flood regime to another. It is proposed that the use of partial series is superior to traditional annual flood series in its practical definition of flooding; provision of a standardized method of analysis which is catholic in its applicability, and parameters which are easily estimated and interpreted.

Simulation of a desalination plant supplied by a photovoltaic array with management strategies*

This paper describes a program developed to simulate and assess the project of a stand-alone seawater desalination plant fed by a photovoltaic array. Objectives of the program are to: • Define the correct dimension for some critical subsystems' models of the plant • Validate some design solutions chosen a priori • Compare different operation management strategies. Simulation of a yearly run of the plant meets the above objec tives, provided suitable models of the system hardware and operation control procedures are available. The program de veloped includes three main sections: (1) models of the system's major components (some models fit the operational character istic of the component, others are limited to the component's transfer function); (2) a stochastic model for prediction of the daily availability of solar energy, based upon the Markov-chain algorithm; and (3) systems control logic. The program has been used to simulate the annual operation of the system under different management strategies. Realistic input data was taken from records of observations from a near by meteorological station. The output data was the performance of the system in terms of electrical energy and drinkable water production in the year. The program was written in Fortran V, run on a Univac 1100/60 computer, and it occupies 11 Kbytes of memory. The simulation of one year of plant operation, with hourly steps, requires 425 Kbytes of memory for input and output data - the computation time amounts to about 9 s. The main routines structure allows utilisation in more general simulation programs in the area of conversion of natural energy resources.

Stochastic dynamic model for long range prediction of monsoon rainfall in Peninsular India

An attempt has been made to develop a Stochastic dynamic model that could be used for forecasting monsoon rainfall, June to September, in the larger sub-division of India, viz., Peninsula. For building such a model the atmosphere bas been considered as a linear dynamic system that converts various inputs into the output, say the rainfall. In this study the 500 mb mean April sub-tropical ridge position along Long. 75° E has been used as input to the atmosphere. The input~ output data for the recent 38 years (1939-1976) have been utilised for developing the model which utilises the dynamics of the atmosphere and also that of the ARIMA process to forecast the rainfall.
 
 The performance of the model has been found good during the sample and the test (1977 to 1980) periods. Even in rank drought and excess rainfall years the closeness of the predicted and realised values stands out well. In terms of seven categories currently being used by the India Meteorological Department for describing its long range forecasts, the skill score of the model forecast for the test period has been found equal to one which is the highest that a forecast formula can have. This suggests that the Stochastic Dynamic Model developed here can therefore, be used for issuing more accurate long range monsoon rainfall forecasts about a month ahead of the season for the Peninsula. This would provide enough time for planning adequate strategies for mitigating the disastrous effects that are produced due to the large vagaries of monsoon.

Stochastic models for prediction of bod and do in streams

A general stochastic model utilizing the concepts of random differential equations is developed. Formulas for the joint probability distribution of BOD and DO at distance t downstream from a pollution source are derived from the assumptions of the model. Finally, some examples and computations are presented which illustrate the usefulness of the model.

Introduction of the characteristic function in stochastic dynamic modelling

The probability characteristic function is used to generalize Epstein's stochastic dynamic prediction model. The time-differencing scheme results in an infinite system of equations which is closed by a quasi-normal procedure. This system yields an explicite approximation to the common or arbitrary marginal characteristic function, which in turn may be transformed to an estimation of the density function. The results are illustrated for Lorenz's minimum hydrodynamic equations. DOI: 10.1111/j.2153-3490.1978.tb00848.x