检索结果-内蒙古大学图书馆

Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin: Application of Monte-Carlo method

引用

ECOLOGICAL INDICATORS 2025年 170卷

作者： Masteali, Sahar Heidari Bayat, Mahmoud Bettinger, Pete Ghorbanpour, Mansour Univ Tehran Fac Nat Resources Dept Environm Tehran Iran Educ & Extens Org AREEO Res Inst Forests & Rangelands Agr Res Tehran Iran Univ Georgia Sch Forestry & Nat Resources Athens GA 30602 USA Arak Univ Fac Agr & Nat Resources Dept Med Plants Arak *** Iran

The assessment of uncertainty in water quality prediction models is crucial for understanding the reliability of these models. This research focuses on the southern basin area of the Caspian Sea in Iran, where there have been limited studies on uncertainty analysis in water quality models. First, a forest layer was extracted from land cover map and 25 large watersheds were selected. River water quality was then assessed from the perspective of 8 landscape connectivity metrics and 12 graph theory indices. We developed predictive models using stepwise linear regression, power, exponential, and logarithmic models to locate the best model form for each water quality parameter (dependent variable) we examined. The goal of this study was to analyze the uncertainty in both linear and non-linear water quality regression models for this region. The researchers used a Monte Carlo simulation method, which is a common approach to investigate uncertainty. First, they determined the best distribution of the independent variables in the regression models using the Kolmogorov-Smirnov goodness of fit test. Then, they generated 15,000 random data points based on the characteristics of the identified variable distributions. In the third stage, new regression models were created using the data from the previous stages. Probability density functions related to the output of the models were developed, and uncertainty analysis was conducted based on the behavior of the models. The results indicated that the CO3 model had the lowest uncertainty bandwidth and the highest R2 among other models. Additionally, the K models had less bandwidth than the other models developed. The research results showed that more than 70% of the models produced results within an acceptable range of uncertainty. Furthermore, the normalized RMSE (NRMSE) values associated with the models were all in the range of 0 to 30%, which is considered acceptable. These outcomes suggest that landscape indicators and graph the

关键词： Uncertainty linear regression non-linear regression Landscape Water quality

来源：评论

学校读者我要写书评

暂无评论

Settlement prediction of a high embankment based on non-linear regression and neural network algorithm

引用

TRANSPORTATION GEOTECHNICS 2025年 50卷

作者： Yang, Enhui Wang, Kai He, Jinhong Liu, Kaiwen Wang, Junxin Zhang, Haopeng Qiu, Yanjun Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China Sichuan Zhenguang Expressway CO LTD Chengdu 610041 Peoples R China Highway Engn Lab Sichuan Prov Chengdu 610031 Sichuan Peoples R China Minist Educ Key Lab High Speed Railway Engn Chengdu 610031 Peoples R China

This study reports in-situ measured data from single-point settlement meters buried in layers at the road shoulders and the center of a high embankment respectively. Based on the measured data, the nonlinear fitting models and neural network algorithm were proposed to establish the method of settlement prediction, and the mean absolute percentage error (MAPE) and mean square errors (MSE) were used to evaluate the accuracy of the settlement prediction model. The results show that the MAPE value of the exponential curve prediction model is less than 10%, and the MAPE value of the hyperbolic model is between 10% and 20%, and the MAPE value of the logarithmic model is greater than 20%, which shows that the exponential curve model of the three nonlinear fitting models has the highest accuracy. After establishing the back propagation (BP) neural network model, the settlement data of the four monitoring points were learned and trained, and the prediction accuracy of the two BP neural network prediction models and the exponential curve prediction model were compared. The model fitting coefficient of R 2 of BP neural network were both greater than 0.99, and the MSE and MAPE were less than 1%. In addition, the multi-step rolling BP neural network prediction model has the highest prediction accuracy, followed by the BP neural network prediction model based on influencing factors while the exponential curve prediction model has the worst performance and weak practicability. This research can provide new inspiration for embankment settlement prediction and give technical support to monitor the disaster of high embankment.

关键词： High embankment Settlement prediction On-site measurement non-linear regression Neural network algorithm

来源：评论

学校读者我要写书评

暂无评论

non-linear regression model for wind turbine power curve

引用

RENEWABLE ENERGY 2017年第Dec.期113卷 732-741页

作者： Marciukaitis, Mantas Zutautaite, Inga Martisauskas, Linas Joksas, Benas Gecevicius, Giedrius Sfetsos, Athanasios Lithuanian Energy Inst Breslaujos G 3 LT-44403 Kaunas Lithuania Vytautas Magnus Univ Vileikos G 8 LT-44404 Kaunas Lithuania Natl Ctr Sci Res Demokritos Inst Nucl & Radiol Sci & Technol Energy & Safety Aghia Paraskevi 15310 Greece

In this article, a study of wind turbine power curve modelling is presented with application to a particular wind turbine of Seirijai wind farm in Lithuania. A non-linear regression model for wind turbine power curve approximation was proposed, which stands out with several advantages, such as fitting physical properties of wind turbine (i.e., power curve does not exceed the highest value of generated power as it is maximum physically possible), lower number of parameters to be estimated, dependency on only one factor. MAPE was used as a measure of approximation method accuracy. Mode approach was introduced as an alternative to typical techniques for modelling power curves of wind turbines with the aim to avoid elimination of the outliers from initial data and the impact of varying concentration of observations in the full range of wind speed. Performed cross-validation analysis demonstrated that the developed power curve model is appropriate for the prediction of wind power and is not directly dependent on the initial data set. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Wind energy Wind power curve non-linear regression Cross-validation

来源：评论

学校读者我要写书评

暂无评论

non-linear regression models for Approximate Bayesian Computation

引用

STATISTICS AND COMPUTING 2010年第1期20卷 63-73页

作者： Blum, Michael G. B. Francois, Olivier Fac Med Grenoble TIMC IMAG CNRS F-38706 La Tronche France Fac Med Grenoble TIMC IMAG Inst Natl Polytech Grenoble F-38706 La Tronche France

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.

关键词： Likelihood-free inference Curse of dimensionality Feed forward neural networks Heteroscedasticity Coalescent models Approximate Bayesian computation Conditional density estimation Implicit statistical models Importance sampling non-linear regression Indirect inference

来源：评论

学校读者我要写书评

暂无评论

non-linear regression analysis with errors in both variables: Estimation of co-operative binding parameters

引用

BIOPHARMACEUTICS & DRUG DISPOSITION 2000年第1期21卷 7-14页

作者： Valsami, G Iliadis, A Macheras, P Univ Mediterranee Fac Pharm Dept Pharmacokinet F-13385 Marseille 5 France Univ Athens Sch Pharm Athens Greece

Four different parameter estimation criteria, the geometric mean functional relationship (GMFR), the maximum likelihood (ML), the perpendicular least-squares (PLS) and the non-linear weighted least squares (WLS), were used to fit a model to the observed data when both regression variables were subject to error. Performances of these criteria were evaluated by fitting the co-operative drug-protein binding Hill model on simulated data containing errors in both variables. Six types of data were simulated with known variances. Comparison of the criteria was done by evaluating the bias, the relative standard deviation (S.D.) and the root-mean-squared error (RMSE), between estimated and true parameter values. Results show that (1) for data with correlated errors, all criteria perform poorly;in particular, the GMFR and ML criteria. For data with uncorrelated errors, all criteria perform equally well with regard to the RMSE. (2) Use of GMFR and ML lead to lower values far S.D. but higher biases compared with WLS and PLS. (3) WLS performs less well when equal dispersion is applied to the two observed variables. Copyright (C) 2000 John Wiley & Sons, Ltd.

关键词： co-operative drug-protein binding model errors-in-variables model estimation criteria non-linear regression

来源：评论

学校读者我要写书评

暂无评论

non-linear regression Forecast Model and Learning Algorithm Based on Functional Networks

Non-linear Regression Forecast Model and Learning Algorithm ...

引用

International Conference on Micro Nano Devices, Structure and Computing Systems

作者： Liu, Xianghu Coll Yuncheng Dept Appl Math Yuncheng Shanxi Peoples R China

ISBN: (纸本)9783037850091

Fitting of forecast function is very difficult and important in non-linear regression forecast problems. The accuracy is directly affected by the fitting of forecast function. linear model replaced non-linear model in the traditional method is difficult to solve the problem when non-linear is stronger, and the result of fitting and forecast is not ideal. Functional network is a recently introduced extension of neural networks. It has certain advantages solving non-linear problems. non-linear regression forecast model and learning algorithm based on functional networks is proposed in this article. Example about multi-variable non-linear regression forecast is provided. The simulation results demonstrate that forecast model based on Functional Networks whose accuracy of fitting and forecasting is more than some traditional methods have some value about theory and application.

关键词： Functional Networks non-linear regression Forecast Learning Algorithm

来源：评论

学校读者我要写书评

暂无评论

linear and non-linear regression analysis for the sorption kinetics of Tropaeoline 000 onto Nano Talc

引用

INTERNATIONAL JOURNAL OF ENVIRONMENTAL ANALYTICAL CHEMISTRY 2022年第9期102卷 2152-2167页

作者： Sharma, Pooja Jain, Rajeev Premi, Apresh Kumar, Neeraj Parashar, Arvind Jiwaji Univ Dept Chem Gwalior India Pondicherry Univ Dept Chem Pondicherry India

A comparison of linear least-squares method and a trial and error non-linear method of three widely used isotherms, Langmuir, Freundlich, and Temkin, were examined. The batch sorption model, based on a pseudo-second-order mechanism, was applied to predict the rate constant of sorption, the equilibrium capacity and the initial sorption rate with the effects of the initial solution pH (4.5) and adsorbent dose. The calculated adsorption capacity (q(e)) was q(e) = 97.6 mg/g. Langmuir isotherm parameters obtained from the Langmuir linear equations were used to fit the adsorption equilibrium data. The adsorption capacity of the adsorbent, obtained from the Langmuir model, was up to 1.78 mol/g(-1). In order to optimise the adsorption-isotherm model, correlation coefficient (R-2) of 0.991 and 0.841 error functions were employed to facilitate the evaluation of fitting accuracy.

关键词： Tropoaeoline 000 linear regression non-linear regression pseudo-second-order kinetics nano talc

来源：评论

学校读者我要写书评

暂无评论

The use of genetic algorithms in the non-linear regression of immittance data

引用

JOURNAL OF ELECTROANALYTICAL CHEMISTRY 1998年第1期448卷 17-23页

作者： VanderNoot, TJ Abrahams, I Univ London Queen Mary & Westfield Coll Dept Chem London E1 4NS England

A genetic algorithm (GA) approach to curve fitting of immittance data is presented. This approach offers a solution to all of the problems associated with traditional non-linear regression of immittance data, such as multiple local minima, the inability to constrain the fitting parameters, and the need for initial estimates of the fitting parameters. The GA works with a 'population' of possible answers (e.g. sets of parameter values). Because of this, it does not require initial estimates of the fitting parameters, but requires only the allowable range of each parameter. Constraints are easily included by rejecting members of the population which fall outside the allowable range for one or more parameters. The fact that there is a population of answers and gradients are not calculated, means that it is more difficult, but not impossible, for a GA to become trapped in a local minimum unlike the more conventional gradient methods. The fitting of simulated noisy Randles data was used to illustrate the method. Populations of 100 individuals were used. The genetic operators were mutation, crossover and a pair of novel line operators. These were selected for use with probabilities, respectively, of 40, 40 and 20% each. A global fit to the data could be achieved within 20000 function evaluations which took 1 min on a 100-MHz 486 PC. Uncertainties were calculated numerically by locating a specified number of points which lay upon the 95% confidence hypersurface. The performance of the GA was compared to that of a quasi-Newton algorithm which calculated the gradients numerically. The quasi-Newton algorithm typically required approximately 2000 function evaluations to converge, but it often converged to a local minimum especially with noisier data. (C) 1998 Elsevier Science S.A. All rights reserved.

关键词： non-linear regression impedance genetic algorithm evolutionary strategy

来源：评论

学校读者我要写书评

暂无评论

Robust complex non-linear regression method for the estimation of equivalent circuit parameters of the thickness-shear-mode acoustic wave sensor

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 1999年第1期48卷 71-80页

作者： Tan, HW Su, XL Wei, WZ Yao, SZ Hunan Univ Chem & Chem Engn Coll New Mat Res Inst Changsha 410082 Peoples R China Changsha Railway Univ Dept Math Phys & Mech Changsha 410075 Peoples R China

Fluctuation in characteristic parameters of the equivalent circuit for the thickness-stear-mode (TSM) acoustic wave sensor is a troublesome problem encountered in their practical applications. It is due to interference from normal and non-normal noise of an impedance analyzer. A robust complex non-linear regression, called complex least trimmed squares regression (CLTSR), is described and utilized in the parameter estimation to alleviate the fluctuation in this article. The results for simulated data indicated that, when the noise distribution was not normal, the CLTSR yields the more robust results than the ordinary complex least squares regression (OCLSR), when the noise distribution was normal, almost the same estimates can be achieved with these two regression methods. The results for the real data indicated that, the noise in experiment could not be regarded as normal distribution. The CLTSR can eliminate the system error and alleviate the parameter fluctuation, especially fluctuation in L-m and C-m. The robust method, therefore, provides a safe alternative to OCLSR and other widely used methods. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： equivalent circuit parameter thickness-shear-mode acoustic wave sensor non-linear regression

来源：评论

学校读者我要写书评

暂无评论

Development of a water quality assessment model: a water quality assessment model based on watershed characteristics by non-linear regression

引用

WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2015年第2期15卷 236-247页

作者： Cho, Yongdeok Korea Water Forum Seoul South Korea

Here, a water quality assessment model (WQAM) is developed by non-linear regression as an alternative to physical watershed modeling in South Korea. Three cases and 10 scenarios are applied and reviewed to determine the most appropriate WQAM. The three cases are: (1) the area size allocation of sub-watersheds, (2) the watershed imperviousness ratio, and (3) the combination of the area size and imperviousness ratio. The 10 scenarios are: (1) impervious, (2) impervious vertical bar pervious, (3) impervious + rainfall, (4) impervious + slope, (5) impervious + rainfall + slope, (6) slope, (7) land usage, (8) land usage + rainfall, (9) land usage + slope, and (10) land usage + rainfall + slope. The best WQAMs are subsequently developed from the generated equations using statistics (R-2, Adjusted R2, F-test, the Akaike information criterion and the Shapiro-Wilk test). In addition, the WQAM is verified using the Geum-Sum-Young River watershed. The percentage differences of biochemical oxygen demand (BOD), total nitrogen (T-N), and total phosphorus (T-P) are 31.66%, 8.08%, and 48.94%, respectively. The developed WQAM can be used in place of complex watershed modeling and to aid in the determination of the best restoration locations.

关键词： non-linear regression statistics water quality assessment model watershed characteristics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：