A Study on Deformation Trend of Concrete Dams

2011 ◽  
Vol 137 ◽  
pp. 82-86
Author(s):  
Jing Lin Qian ◽  
Ye Zhang ◽  
Min Sheng Zheng
Keyword(s):  
Regression Models ◽  
Wavelet Decomposition ◽  
Regression Method ◽  
Characteristic Polynomials ◽  
Concrete Dam ◽  
Trend Component ◽  
Characteristic Roots ◽  
Observation Series ◽  
Dam Deformation ◽  
Auto Regression

The observation series of concrete dam deformation is composed of the trend component, the periodic component and the stochastic component.In this paper, several stages are separated from the whole runtime according to the state of the dam.The trend component at each stage is extracted with the wavelet decomposition method, and modeled by the auto-regression method respectively. Then the characteristic polynomials of the auto-regression models are built. When solving these characteristic polynomials, the characteristic roots can be got. At last, to make a decision whether the dam is safe or not according to the trend of the minimum moduli of the roots. The application example shows that the conclusions of the method are consistent with the fact.

scholarly journals Ensemble of bagged regression trees for concrete dam deformation predicting

2019 ◽  
Vol 376 ◽  
pp. 012040 ◽  
Author(s):  
Jiemin Xie ◽  
Jun Zhang ◽  
Xuan Xie ◽  
Zhiwei Bi ◽  
Zhuoheng Li
Keyword(s):  
Regression Trees ◽  
Concrete Dam ◽  
Dam Deformation

scholarly journals Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables

2020 ◽  
Vol 13 (9) ◽  
pp. 189 ◽  
Author(s):  
Ahmed Ibrahim ◽  
Rasha Kashef ◽  
Menglu Li ◽  
Esteban Valencia ◽  
Eric Huang
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Vector Autoregression ◽  
State Of The Art ◽  
Bayesian Regression ◽  
Major Question ◽  
Factors Of Influence ◽  
Vector Auto Regression ◽  
Auto Regression ◽  
Price Discovery Process

The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models.

Prediction Model of Concrete Dam Deformation Based on Adaptive Unscented Kalman Filter and BP Neural Network

2014 ◽  
Vol 513-517 ◽  
pp. 4076-4079 ◽  
Author(s):  
Liang Hui Li ◽  
Sheng Jun Peng ◽  
Zhen Xiang Jiang ◽  
Bo Wen Wei
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Unscented Kalman Filter ◽  
Safety Monitoring ◽  
Hydraulic Engineering ◽  
Concrete Dam ◽  
Adaptive Unscented Kalman Filter ◽  
Dam Deformation

By using unscented kalman filter (UKF) theory and introducing adaptive factor into BP neural network, a new prediction model of concrete dam deformation was proposed. Example shows that this model can improve the convergence speed of BP neural network, and the calculation precision of this model meets engineering requirements. Meanwhile, this model can be applied in the safety monitoring of other hydraulic engineering structure.

Introducing Quantile Mapping to a Regression Model Using a Multi-Model Ensemble to Improve Probabilistic Projections of Monthly Precipitation

2018 ◽  
Vol 13 (5) ◽  
pp. 873-878
Author(s):  
Noriko N. Ishizaki ◽  
Koji Dairaku ◽  
Genta Ueno ◽  
◽  
Keyword(s):  
Regression Models ◽  
Model Development ◽  
Regression Method ◽  
Monthly Precipitation ◽  
Model Ensemble ◽  
Quantile Mapping ◽  
Estimation Errors ◽  
The Difference ◽  
Probabilistic Projection ◽  
Probabilistic Projections

A new method was proposed for the probabilistic projection of future climate that introduced quantile mapping to a regression method using a multi-model ensemble (QM_RMME). Results of this method were then compared with those of the traditional regression method (RMME). Six stations in Japan where 100 year observation records were available were used to evaluate the performance of the methods. An initial 50-year period (1901–1950) was used to develop the regression models and the final period (1951–2000) was used for evaluation. Results showed that the estimation errors at the 50th and 90th percentile were smaller for QM_RMME as compared to RMME at most sites. Conversely, when the model development and evaluation periods were limited to 20 years (1901–1920 and 1951–1970, respectively), the 90th percentile error was larger for QM_RMME. This was attributed to quantile mapping resulting in over-fitting of the data during the model development period. Furthermore, the QM_RMME error increased when the difference of observations between the model development and verification periods was large. Therefore, results indicated that the RMME method was more stable for relatively short data verification periods.

Study on Establishing Regression Model to Predict Leg Girth of Young Women

2011 ◽  
Vol 120 ◽  
pp. 528-532
Author(s):  
Yi Jie Zhang ◽  
Fei Yuan ◽  
Jie Sun ◽  
Juan Feng Jin ◽  
Feng Yuan Zou
Keyword(s):  
Regression Model ◽  
Young Women ◽  
Regression Models ◽  
Measurement Data ◽  
Multiple Linear Regression Model ◽  
Regression Method ◽  
Quadratic Regression ◽  
Compression Stockings ◽  
Best Fitting ◽  
Spss Software

Using Martin Meter from Japan to measure 110 young women body. Using SPSS software for correlation analysis of measurement data, then selecting width, thickness and weight as elements used for establishing leg girth regression models. Adopting backward regression method to choose variable further, and establishing Multiple Linear regression model, Multivariate Quadratic regression model, Multivariate Cubic regression model to predict the girth of each part of leg which related to clothing. After testing and comparing the effect of each model, choosing out the best fitting modle for each part. The validity of modles were tested by randomly selecting 20 young women’s leg data,comparing with manual measurements, gaining satisfactory results. So as to provide an important technical support for human leg two-dimensional non-contact measurement and be further applied to research on medical compression stockings.

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