Uncertainty Analysis of Flood Forecasting in River Channel

2012 ◽  
Vol 550-553 ◽  
pp. 2489-2492
Author(s):  
Qun Hao ◽  
Ying Na Sun ◽  
Ning Jiang
Keyword(s):  
Uncertainty Analysis ◽  
Normal Distribution ◽  
Flood Control ◽  
Random Variable ◽  
Flood Forecasting ◽  
River Channel ◽  
Statistical Characteristics ◽  
Forecasting Model ◽  
Forward Algorithm ◽  
Linear Characteristic

In this paper, the stochastic differential equations theory was used to analyze the uncertainty of flood forecasting in river channel based on the forward algorithm of linear characteristic. And then a river channel flood forecasting model, in which the coefficient of storage and discharge was regarded as a random variable, was built. The statistical characteristics of outflow process could be taken part in theory by the built river channel flood forecasting model when the coefficient of storage obeyed a kind of normal distribution. Storage coefficient is random variable in the model. The results showed that the uncertainty degree of outflow process could be made through considering the uncertainty of river channel flood forecasting, which would provide some references for making decision in flood control.

scholarly journals Flood forecasting using an improved NARX network based on wavelet analysis coupled with uncertainty analysis by Monte Carlo simulations: a case study of Taihu Basin, China

2021 ◽  
Author(s):  
Feiqing Jiang ◽  
Zengchuan Dong ◽  
Zeng'an Wang ◽  
Yiqing Zhu ◽  
Moyang Liu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Uncertainty Analysis ◽  
Water Level ◽  
Flood Control ◽  
Back Propagation ◽  
Flood Forecasting ◽  
Scientific Basis ◽  
Discrete Wavelet ◽  
Taihu Basin

Abstract Reliable flood forecasting can provide scientific basis for flood risk assessment and water resources management, and the Taihu water level forecasting with high precision is essential for flood control in the Taihu Basin. To increase the prediction accuracy, a coupling model (DWT-iNARX) is established by combining the discrete wavelet transformation (DWT) with improved nonlinear autoregressive with exogenous inputs network (iNARX), for predicting the daily Taihu water level during the flood season under different forecast periods. And the DWT-iNARX model is compared with the back-propagation neural network (BP) and iNARX models to assess its capability in prediction. Meanwhile, we propose an uncertainty analysis method based on Monte Carlo simulations (MCS) for quantifying model uncertainty and performing probabilistic water level forecast. The results show that three models achieve good simulation results with higher accuracy when the forecast period is short, such as 1–3 days. In overall performance, iNARX and DWT-iNARX models show superiority in comparison with the BP model, while the DWT-iNARX model yields the best performance among all the other models. The research results can provide a certain reference for the water level forecast of the Taihu Lake.

scholarly journals Revisiting the Planet Mass and Stellar Metallicity Relation for Low-Mass Exoplanets Orbiting GKM Class Stars

Universe ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 88
Author(s):  
Jonathan H. Jiang ◽  
Daniel Zhao ◽  
Xuan Ji ◽  
Bohan Xie ◽  
Kristen A. Fahy
Keyword(s):  
Uncertainty Analysis ◽  
Stellar Mass ◽  
Observational Evidence ◽  
Statistical Characteristics ◽  
Stellar Disk ◽  
Exponential Law ◽  
Spectral Class ◽  
Low Mass ◽  
M Stars

The growing database of exoplanets has shown us the statistical characteristics of various exoplanet populations, providing insight towards their origins. Observational evidence suggests that the process by which gas giants are conceived in the stellar disk may be disparate from that of smaller planets. Using NASA’s Exoplanet Archive, we analyzed the relationships between planet mass and stellar metallicity, as well as planet mass and stellar mass for low-mass exoplanets (MP < 0.13 MJ) orbiting spectral class G, K, and M stars. We performed further uncertainty analysis to confirm that the exponential law relationships found between the planet mass, stellar mass, and the stellar metallicity cannot be fully explained by observation biases alone.

Statistical Characteristics and Correlation Analysis for Control Index of Soil-Aggregate Mixture

2013 ◽  
Vol 718-720 ◽  
pp. 1872-1877 ◽  
Author(s):  
Xu Xi Chang ◽  
Xie Jian Ming ◽  
Jiang Ling Fa ◽  
Chen Shan Xiong
Keyword(s):  
Grain Size ◽  
Correlation Analysis ◽  
Normal Distribution ◽  
Large Scale ◽  
Soil Aggregate ◽  
Site Preparation ◽  
Statistical Characteristics ◽  
Coarse Grain ◽  
Systematic Research ◽  
Large Variability

Currently, the soil-aggregate mixture has been widely used in some large-scale site preparation projects, compaction characteristics has been pay more attention by many engineers and researchers. However, systematic research is insufficient on how to choose the filler. Moreover, some industry regulations are different on the requirements about filler. This paper relies on a certain big site preparation projects, discussing statistical characteristics and correlation on the maximal grain size, contents of the coarse grain, gradation and other parameters of soil-aggregate mixture. The results show that the maximal and the median grain size have small discreteness and normal distribution, indicating site filler is easy to reach the requirement; The coefficient of curvature, coefficient of nonuniformity and the coarse grain content have large discreteness, and dont obey normal distribution, indicating the filler has large variability. The median grain size is highly relevant to the coarse grain content; the maximal grain size isnt relevant to the coefficient of nonuniformity, the coefficient of curvature and the coarse grain content. According to the results of correlation analysis, we suggest that the importance order follow by coarse grain content, the maximum grain size and gradation for the control parameters of filler. This research may be significant to other similar projects.

scholarly journals ДОСЛІДЖЕННЯ ПОКАЗНИКА ЕФЕКТИВНОСТІ ЕКСПЛУАТАЦІЇ ЗАСОБІВ ВИМІРЮВАЛЬНОЇ ТЕХНІКИ МЕТОДОМ КОМПʼЮТЕРНОГО МОДЕЛЮВАННЯ

2020 ◽  
pp. 166-169
Author(s):  
Олександр Володимирович Томашевський ◽  
Геннадій Валентинович Сніжной
Keyword(s):  
Computer Simulation ◽  
Distribution Function ◽  
Normal Distribution ◽  
Failure Rate ◽  
Random Variable ◽  
Operational Efficiency ◽  
Distribution Law ◽  
Maintenance System ◽  
Wide Range ◽  
Calibration Interval

The operational efficiency of measuring equipment (ME) is important in determining the cost of maintaining ME. To characterize the operational efficiency of the ME, an efficiency indicator has been introduced, an increase of which will reduce costs caused by the release of defective products due to the use of ME with unreliable indications. Over time, the ME parameters change under the influence of external factors and the ME aging processes inevitably occur, as a result of which the parameters of the ME metrological service system change. Therefore, in the general case, the parameters of the metrological maintenance system of ME should be considered as random variables. Accordingly, the efficiency indicator of measuring instruments is also a random variable, for the determination of which it is advisable to apply the methods of mathematical statistics and computer simulation. The performance indicator depends on the parameters of the metrological maintenance ME system, such as the calibration interval, the time spent by the ME on metrological maintenance, and the likelihood of ME failure-free operation. As a random variable, the efficiency indicator has a certain distribution function. To determine the distribution function of the efficiency indicator and the corresponding statistical characteristics, a computer simulation method was used. A study was made of the influence on the indicator of the effectiveness of the parameters of the metrological maintenance system ME (interesting interval, the failure rate of ME). The value of the verification interval and the failure rate of MEs varied over a wide range typical of real production. The time spent by ME on metrological services is considered as a random variable with a normal distribution law. To obtain random numbers with a normal distribution law, the Box-Muller method is used. After modeling, the statistical processing of the obtained results was done. It is shown that in real production, the efficiency indicator has a normal distribution law and the value of the efficiency indicator with an increase in the calibration interval does not practically change.

scholarly journals Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1362 ◽  
Author(s):  
Lu Chen ◽  
Na Sun ◽  
Chao Zhou ◽  
Jianzhong Zhou ◽  
Yanlai Zhou ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Flood Control ◽  
Search Algorithm ◽  
Flood Forecasting ◽  
Data Driven ◽  
General Regression Neural Network ◽  
Promising Alternative ◽  
Backtracking Search ◽  
Learning Machine ◽  
Population Based Algorithm

Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.

scholarly journals Development and Integration of Sub-Daily Flood Modelling Capability within the SWAT Model and a Comparison with XAJ Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1263 ◽  
Author(s):  
Dachen Li ◽  
Simin Qu ◽  
Peng Shi ◽  
Xueqiu Chen ◽  
Feng Xue ◽  
...  
Keyword(s):  
Time Scale ◽  
Flood Control ◽  
Assessment Tool ◽  
Spatial Scales ◽  
Swat Model ◽  
Flood Forecasting ◽  
Base Flow ◽  
Flood Simulation ◽  
Promising Tool ◽  
Daily Time

To date, floods have become one of the most severe natural disasters on Earth. Flood forecasting with hydrological models is an important non-engineering measure for flood control and disaster reduction. The Xin’anjiang (XAJ) model is the most widely used hydrological model in China for flood forecasting, while the Soil and Water Assessment Tool (SWAT) model is widely applied for daily and monthly simulation and has shown its potential for flood simulation. The objective of this paper is to evaluate the performance of the SWAT model in simulating floods at a sub-daily time-scale in a slightly larger basin and compare that with the XAJ model. Taking Qilijie Basin (southeast of China) as a study area, this paper developed the XAJ model and SWAT model at a sub-daily time-scale. The results showed that the XAJ model had a better performance than the sub-daily SWAT model regarding relative runoff error (RRE) but the SWAT model performed well according to relative peak discharge error (RPE) and error of occurrence time of peak flow (PTE). The SWAT model performed unsatisfactorily in simulating low flows due to the daily calculation of base flow but behaved quite well in simulating high flows. We also evaluated the effect of spatial scale on the SWAT model. The results showed that the SWAT model had a good applicability at different spatial scales. In conclusion, the sub-daily SWAT model is a promising tool for flood simulation though more improvements remain to be studied further.

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