Forecasting by Stochastic Models to Inflow of Karkheh Dam at Iran

Forecasting the inflow of rivers to reservoirs of dams has high importance and complexity. Design and optimal operation of the dams is essential. Mathematical and analytical methods use for understanding estimating and prediction of inflow to reservoirs in the future. Various methods including stochastic models can be used as a management tool to predict future values of these systems. In this study stochastic models (ARIMA) are applied to records of mean annual flow Karkheh river entrance to Karkheh dam in the west of Iran. For this purpose we collected annual flow during the period from 1958/1959 to 2005/2006 in Jelogir Majin hydrometric station. The available data consists of 48 years of mean Annual discharge. Three types of ARIMA (p, d, q) models (0, 1, 1), (1, 1, 1) and (4, 1, 1) suggested, and the selected model is the one which give minimum Akaike Information Criterion (AIC). The Maximum Likelihood (ML), Conditional Least Square (CLS) and Unconditional Least Square (ULS) methods are used to estimate the model parameters. It is found that the model which corresponds to the minimum AIC is the (4, 1, 1) model in CLS estimation method. Port Manteau Lack of fit test and Residual Autocorrelation Function (RACF) test are applied as diagnostic checking. Forecasting of annual inflow for the period from 2006 to 2015 are compared with observed inflow for the same period and since agreement is very good adequacy of the selected model is confirmed.

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Parameter Identification Research Based on ARX Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.190-191.292 ◽

2012 ◽

Vol 190-191 ◽

pp. 292-296

Author(s):

Huai Yuan Liu ◽

Jian Hua He ◽

Song Chen

Keyword(s):

System Identification ◽

Programming Model ◽

System Modeling ◽

Estimation Method ◽

Information Criterion ◽

Least Square ◽

Model Parameters ◽

Least Square Estimation ◽

Model Order ◽

Arx Model

Based on the principle of the system identification, combined Simulink with System Identification Toolbox from MATLAB, the least square estimation method is selected to establish a system of ARX model, and Akaike Information Criterion (AIC) was used in the identification of model order, compared with the original model to study the fitting accuracy, and the validity of the model is examined by residual analysis. This approach overcomes the disadvantages of the complexity and difficulty in traditional programming model. Compared to other program identification method, it has a short modeling time, and it is clear, reliable, intuitive visual, good scalability. Furthermore, the model parameters, result and system can be easily modified, assessed and verified. This method of system modeling and simulation can be used for reference to aerospace and other fields.

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Overcoming the Existence of Extreme Outlier Data by Using Robust MM Method Based on The Objective Function of Tukey bisquare

SCIENCE NATURE ◽

10.30598/snvol1iss1pp022-032year2018 ◽

2018 ◽

Vol 1 (1) ◽

pp. 022-032

Author(s):

Science Nature

Keyword(s):

High Efficiency ◽

Robust Regression ◽

Weighting Function ◽

Estimation Method ◽

Breakdown Point ◽

Error Component ◽

Least Square ◽

Model Parameters ◽

Observation Data ◽

Extreme Outlier

A widely used estimation method in estimating regression model parameters is the ordinary least square (OLS) that minimizes the sum of the error squares. In addition to the ease of computing, OLS is a good unbiased estimator as long as the error component assumption () in the given model is met. However, in the application, it is often encountered violations of assumptions. One of the violation types is the violation of distributed error assumption which is caused by the existence of the outlier on observation data. Thus, a solid method is required to overcome the existence of outlier, that is Robust Regression. One of the Robust Regression methods commonly used is robust MM method. Robust MM method is a combination of breakdown point and high efficiency. Results obtained based on simulated data generated using SAS software 9.2, shows that the use of objective weighting function tukey bisquare is able to overcome the existence of extreme outlier. Furthermore, it is determined that the value of tuning constant c with Robust MM method is 4.685 and it is obtained95% of efficiency. Thus, the obtained breakdown point is 50%.

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Overcoming the Existence of Extreme Outlier Data by Using Robust MM Method Based on The Objective Function of Tukey bisquare

SCIENCE NATURE ◽

10.30598/vol1iss1pp022-032year2018 ◽

2018 ◽

Vol 1 (1) ◽

pp. 022-032

Author(s):

Science Nature

Keyword(s):

High Efficiency ◽

Robust Regression ◽

Weighting Function ◽

Estimation Method ◽

Breakdown Point ◽

Error Component ◽

Least Square ◽

Model Parameters ◽

Observation Data ◽

Extreme Outlier

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Determination of Optimal Indicators Based on Statistical Analysis for the State of Health Estimation of a Lithium-Ion Battery

Frontiers in Energy Research ◽

10.3389/fenrg.2021.690266 ◽

2021 ◽

Vol 9 ◽

Author(s):

Gaoya Shi ◽

Siqi Chen ◽

Hao Yuan ◽

Heze You ◽

Xueyuan Wang ◽

...

Keyword(s):

Statistical Analysis ◽

Ambient Temperature ◽

Estimation Method ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Least Square ◽

Model Parameters ◽

Battery Management System ◽

State Of Health ◽

Recursive Least Square

Online state of health (SOH) estimation is essential for lithium-ion batteries in a battery management system. As the conventional SOH indicator, the capacity is challenging to be estimated online. Apart from the capacity, various indicators related to the internal resistance are proposed as indicators for the SOH estimation. However, research gaps still exist in terms of optimal resistance-related indicators, online acquisition of indicators, temperature disturbance elimination, and state of charge (SOC) disturbance elimination. In this study, the equivalent circuit model parameters are identified based on recursive least square method in dynamic working conditions in the life span. Statistical analysis methods including multiple stepwise regression analysis and path analysis are introduced to characterize the sensitivity of the parameters to SOH estimation. Based on the above approach, the coupling relationship between the parameters is comprehensively analyzed. Results indicate that the ohmic resistance R0 and the diffusion capacitance Cd are the most suitable parameters for the SOH indication. Furthermore, R0 and Cd are proved to be exponentially correlated to the ambient temperature, while SOC demonstrates a quadratic trend on them. To eliminate the disturbance caused by the ambient temperature and SOC, a compensating method is further proposed. Finally, a mapping relationship between SOH and the indicators under normal operations is established. SOH can be estimated with the maximum error of 2.301%, which proves the reliability and feasibility of the proposed indicators and estimation method.

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Copula and ARMA based study of controlled outflow at Farakka barrage

10.5194/hess-2018-380 ◽

2018 ◽

Author(s):

Uttam Singh ◽

Venkappayya R. Desai ◽

Pramod K. Sharma ◽

Chandra S. P. Ojha

Keyword(s):

Estimation Method ◽

Information Criterion ◽

Extreme Value ◽

Distribution Functions ◽

Spatial And Temporal Variation ◽

Model Parameters ◽

Copula Model ◽

Discharge Data ◽

Ml Estimation ◽

Farakka Barrage

Abstract. In this study, 25 years mean monthly out flow discharge data of Farakka barrage was used (i.e., from 1949 to 1968). Farakka barrage is located between on Ganga River. Spatial and temporal variation in flow rate for any particular area is very common due to various meteorological and other factors existing in nature. But large variations in these factors cause extreme events (e. g., floods and droughts). Monthly outflow discharge for a particular critical month are predicted using statistical models (ARMA Model and Copula Model). Different Copulas (i.e., Normal, t, Frank, Clayton, Gumbel–Hoggard, Ali–Mikhail–Haq) are used for this purpose and the copula model is selected based on distribution functions (Normal distribution, Lognormal distribution, Extreme value type-1 distribution, Generalized Extreme value type, Gamma distribution, Weibull distribution, Exponential distribution). The distribution is selected based on the Mean square error (MSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model parameters were computed using the Maximum Likelihood (ML) estimation method.

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Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect

Risks ◽

10.3390/risks9010005 ◽

2020 ◽

Vol 9 (1) ◽

pp. 5

Author(s):

Karim Barigou ◽

Stéphane Loisel ◽

Yahia Salhi

Keyword(s):

Life Insurance ◽

Cross Validation ◽

Cohort Effect ◽

Information Criterion ◽

Mortality Forecasting ◽

Hand Model ◽

Regularization Techniques ◽

Bayes Information Criterion ◽

The One ◽

Regularized Model

Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability.

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Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

Journal of Elasticity ◽

10.1007/s10659-021-09833-9 ◽

2021 ◽

Author(s):

Sebastian Brandstaeter ◽

Sebastian L. Fuchs ◽

Jonas Biehler ◽

Roland C. Aydin ◽

Wolfgang A. Wall ◽

...

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Model Parameters ◽

Computational Studies ◽

Growth And Remodeling ◽

Global Sensitivity ◽

Output Variability ◽

Constrained Mixture ◽

The One ◽

Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

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Model of the influence of Internet finance on monetary policy based on gibbs sampling and vector autoregression

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189489 ◽

2020 ◽

pp. 1-11

Author(s):

Hui Wang ◽

Huang Shiwang

Keyword(s):

Monetary Policy ◽

Gibbs Sampling ◽

Posterior Distribution ◽

Estimation Method ◽

Financial Supervision ◽

Var Model ◽

Model Parameters ◽

State Variables ◽

Frequency Data ◽

Internet Finance

The various parts of the traditional financial supervision and management system can no longer meet the current needs, and further improvement is urgently needed. In this paper, the low-frequency data is regarded as the missing of the high-frequency data, and the mixed frequency VAR model is adopted. In order to overcome the problems caused by too many parameters of the VAR model, this paper adopts the Bayesian estimation method based on the Minnesota prior to obtain the posterior distribution of each parameter of the VAR model. Moreover, this paper uses methods based on Kalman filtering and Kalman smoothing to obtain the posterior distribution of latent state variables. Then, according to the posterior distribution of the VAR model parameters and the posterior distribution of the latent state variables, this paper uses the Gibbs sampling method to obtain the mixed Bayes vector autoregressive model and the estimation of the state variables. Finally, this article studies the influence of Internet finance on monetary policy with examples. The research results show that the method proposed in this article has a certain effect.

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An Alternative Promotion Time Cure Model with Overdispersed Number of Competing Causes: An Application to Melanoma Data

Mathematics ◽

10.3390/math9151815 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1815

Author(s):

Diego I. Gallardo ◽

Mário de Castro ◽

Héctor W. Gómez

Keyword(s):

Estimation Method ◽

Selection Criterion ◽

Likelihood Method ◽

Simulation Studies ◽

Cure Rate ◽

Nested Models ◽

Proposed Model ◽

The Em Algorithm ◽

Competing Causes ◽

The One

A cure rate model under the competing risks setup is proposed. For the number of competing causes related to the occurrence of the event of interest, we posit the one-parameter Bell distribution, which accommodates overdispersed counts. The model is parameterized in the cure rate, which is linked to covariates. Parameter estimation is based on the maximum likelihood method. Estimates are computed via the EM algorithm. In order to compare different models, a selection criterion for non-nested models is implemented. Results from simulation studies indicate that the estimation method and the model selection criterion have a good performance. A dataset on melanoma is analyzed using the proposed model as well as some models from the literature.

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Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

Remote Sensing ◽

10.3390/rs13152997 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2997

Author(s):

Zheng Zhao ◽

Weiming Tian ◽

Yunkai Deng ◽

Cheng Hu ◽

Tao Zeng

Keyword(s):

Multiple Input Multiple Output ◽

Estimation Method ◽

Target Position ◽

Position Estimation ◽

Least Square ◽

Antenna Structure ◽

Imaging Radar ◽

Position Errors ◽

Calibration Methods ◽

Array Errors

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

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