Sensitivity analysis of the probability distribution of groundwater level series based on information entropy

2012 ◽  
Vol 26 (3) ◽  
pp. 345-356 ◽  
Author(s):  
Xiankui Zeng ◽  
Dong Wang ◽  
Jichun Wu
Keyword(s):  
Sensitivity Analysis ◽  
Probability Distribution ◽  
Information Entropy ◽  
Groundwater Level

scholarly journals Climatic and basin factors affecting the flood frequency curve: PART II – A full sensitivity analysis based on the continuous simulation approach combined with a factorial experimental design

2000 ◽  
Vol 4 (3) ◽  
pp. 483-498 ◽  
Author(s):  
M. Franchini ◽  
A. M. Hashemi ◽  
P. E. O’Connell
Keyword(s):  
Sensitivity Analysis ◽  
Soil Moisture ◽  
Experimental Design ◽  
Probability Distribution ◽  
Flood Frequency ◽  
Rainfall Regime ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Factorial Experimental Design ◽  
Frequency Curve

Abstract. The sensitivity analysis described in Hashemi et al. (2000) is based on one-at-a-time perturbations to the model parameters. This type of analysis cannot highlight the presence of parameter interactions which might indeed affect the characteristics of the flood frequency curve (ffc) even more than the individual parameters. For this reason, the effects of the parameters of the rainfall, rainfall runoff models and of the potential evapotranspiration demand on the ffc are investigated here through an analysis of the results obtained from a factorial experimental design, where all the parameters are allowed to vary simultaneously. This latter, more complex, analysis confirms the results obtained in Hashemi et al. (2000) thus making the conclusions drawn there of wider validity and not related strictly to the reference set selected. However, it is shown that two-factor interactions are present not only between different pairs of parameters of an individual model, but also between pairs of parameters of different models, such as rainfall and rainfall-runoff models, thus demonstrating the complex interaction between climate and basin characteristics affecting the ffc and in particular its curvature. Furthermore, the wider range of climatic regime behaviour produced within the factorial experimental design shows that the probability distribution of soil moisture content at the storm arrival time is no longer sufficient to explain the link between the perturbations to the parameters and their effects on the ffc, as was suggested in Hashemi et al. (2000). Other factors have to be considered, such as the probability distribution of the soil moisture capacity, and the rainfall regime, expressed through the annual maximum rainfalls over different durations. Keywords: Monte Carlo simulation; factorial experimental design; analysis of variance (ANOVA)

scholarly journals A Survey of Information Entropy Metrics for Complex Networks

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1417
Author(s):  
Yamila M. Omar ◽  
Peter Plapper
Keyword(s):  
Probability Distribution ◽  
Literature Review ◽  
Complex Networks ◽  
Information Entropy ◽  
Future Research ◽  
Wide Range ◽  
Growing Body ◽  
Narrative Literature ◽  
Narrative Literature Review ◽  
Further Development

Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts.

Parametric Study of Nanostructure Variations on the Macroscopic Thermal and Electrical Behavior of Neat CNT Thin Film Networks

2011 ◽  
Author(s):  
Nikhil A. Ashtekar ◽  
David A. Jack
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Electrical Conductivity ◽  
Sensitivity Analysis ◽  
Carbon Nanotube ◽  
Probability Distribution ◽  
Electrical Response ◽  
Electrical Behavior ◽  
Commercial Applications ◽  
Nanotube Chirality

Carbon nanotube thin films are considered by many researchers as a material for the future in many electrical and thermal applications, but a lack of systematic physics-based modeling approaches to quantify the bulk thermal and electrical response due to nanostructure variations makes employing these thin films difficult for commercial applications. In this work we employ the previously presented 3D physics-based computational model for characterizing the bulk thermal and electrical response of a neat carbon nanotube thin film network involving stochastic distributions of length, diameter, chirality, orientation and values of intercontact resistivity obtained from the literature. The model is employed to test the sensitivity of bulk thermal and electrical conductivity on stochastic variations in the nanostructure parameters. We examine the sensitivity of the thin film networks to the experimentally obtained Weibull probability distribution for length and diameter. Additionally, we present a study to quantify the macroscopic conductivity dependence on the nanotube chirality ratio. Through these studies we present an approach that is very generic and can be used for the sensitivity analysis due to variations within the nanostructure.

scholarly journals A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5383
Author(s):  
Yuting Hou ◽  
Xiang Li ◽  
Yang Zheng ◽  
Jinjie Zhou ◽  
Jidong Tan ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Probability Distribution ◽  
Confidence Interval ◽  
Quantitative Description ◽  
Superior Performance ◽  
Internal Stresses ◽  
Barkhausen Noise ◽  
Great Effort ◽  
Parameter Uncertainties ◽  
Leibler Divergence

The magnetic Barkhausen noise (MBN) signal provides interesting clues about the evolution of microstructure of the magnetic material (internal stresses, level of degradation, etc.). This makes it widely used in non-destructive evaluation of ferromagnetic materials. Although researchers have made great effort to explore the intrinsic random characteristics and stable features of MBN signals, they have failed to provide a deterministic definition of the stochastic quality of the MBN signals. Because many features are not reproducible, there is no quantitative description for the stochastic nature of MBN, and no uniform standards to evaluate performance of features. We aim to make further study on the stochastic characteristics of MBN signal and transform it into the quantification of signal uncertainty and sensitivity, to solve the above problems for fatigue state prediction. In the case of parameter uncertainty in the prediction model, a prior approximation method was proposed. Thus, there are two distinct sources of uncertainty: feature(observation) uncertainty and model uncertainty were discussed. We define feature uncertainty from the perspective of a probability distribution using a confidence interval sensitivity analysis, and uniformly quantize and re-parameterize the feature matrix from the feature probability distribution space. We also incorporate informed priors into the estimation process by optimizing the Kullback–Leibler divergence between prior and posterior distribution, approximating the prior to the posterior. Thus, in an insufficient data situation, informed priors can improve prediction accuracy. Experiments prove that our proposed confidence interval sensitivity analysis to capture feature uncertainty has the potential to determine the instability in MBN signals quantitatively and reduce the dispersion of features, so that all features can produce positive additive effects. The false prediction rate can be reduced to almost 0. The proposed priors can not only measure model parameter uncertainties but also show superior performance similar to that of maximum likelihood estimation (MLE). The results also show that improvements in parameter uncertainties cannot be directly propagated to improve prediction uncertainties.

Probability Model of Corrosion Initiation Time of Steel in Concrete Structure in Chloride Environment

2011 ◽  
Vol 243-249 ◽  
pp. 5632-5636
Author(s):  
Ya Li Ma ◽  
Ai Lin Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Probability Distribution ◽  
Probability Model ◽  
Chloride Concentration ◽  
Initiation Time ◽  
Distribution Law ◽  
Corrosion Initiation ◽  
Corrosion Initiation Time ◽  
Chloride Environment ◽  
Distribution Type

Probability distribution law of corrosion initiation time of steel in concrete under chloride environment is discussed. Based on the Fick’s second law, by Monte Carlo, frequency distribution, distribution type and probability density is analyzed. The statistic parameters of the factors influencing the probability distribution of corrosion initiation time are studied and the expression for sensitivity analysis of corrosion initiation time is deduced. By sensitivity analysis can know, corrosion initiation time is found to be more sensitive to cover than the diffusion coefficient, and more sensitive to surface chloride concentration than the critical chloride level. The analysis of the paper perfects the methods of predicting the corrosion initiation time.

Predicting the critical path changes using sensitivity analysis: a delay analysis approach

2020 ◽  
Vol 13 (5) ◽  
pp. 1097-1119
Author(s):  
Mohammed Hammad ◽  
Alireza Abbasi ◽  
Ripon K. Chakrabortty ◽  
Michael J. Ryan
Keyword(s):  
Sensitivity Analysis ◽  
Probability Distribution ◽  
Critical Path ◽  
Research Work ◽  
Delay Analysis ◽  
Content Type ◽  
Project Completion ◽  
Analysis Scheme ◽  
Completion Date ◽  
Distribution Type

PurposeThis research presents a framework that allows project managers to predict the next critical paths (CP(s)) and to take extra care when planning and executing those activities that have the potential to cause changes in a project's current CP(s).Design/methodology/approachThe method presented here is based on an assessment of each activity's contribution to the overall schedule variance, which involves assigning a probability distribution function to each activity duration in the project. A sensitivity analysis is also carried out, which forms the basis of identifying which activity most affects the project completion date and therefore will have the greatest effect in changing the CP.FindingsThe authors’ analysis reveals that the most appropriate probability density function (PDF) for the targeted project is the normal distribution. However, the aim of this work is not to determine the most suitable distribution for each activity but rather to study the effect of the activity distribution type on the CP prediction. The results show that the selection of the appropriate probability distribution is very important, since it can impact the CP prediction and estimated project completion date.Originality/valueThis research work proposes a delay analysis scheme which can help the project manager to predict the next CP and to improve performance by identifying which activity is the bottleneck. On the other hand, the simplicity arises from the fact that this method does not require any expensive machines or software to generate results.

Appraising groundwater potential zones using geospatial and multi-criteria decision analysis (MCDA) techniques in Andasa-Tul watershed, Upper Blue Nile basin, Ethiopia

2021 ◽  
Author(s):  
Zelalem Leyew Anteneh ◽  
Melkam Meseret Alemu ◽  
Getnet Taye Bawoke ◽  
Alebachew Tareke Kehali ◽  
Mulugeta Chanie Fenta ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Decision Analysis ◽  
Groundwater Level ◽  
Groundwater Potential ◽  
Nile Basin ◽  
Blue Nile ◽  
Thematic Layers ◽  
Blue Nile Basin ◽  
Variation Index ◽  
Upper Blue Nile Basin

Abstract Ever increase in population growth and drastic climatic changes augment the demand and exploration of groundwater from time to time. An integrated approach of remote sensing (RS), geographic information system (GIS) and multicriteria decision analysis (MCDA) of analytical hierarchical process (AHP) were applied to delineate groundwater potential (GWP) zones in Andasa-Tul watershed, Upper Blue Nile Basin, Ethiopia. For this purpose, nine GWP influencing thematic layers comprising lithology, lineament density, geomorphology, slope, soil, drainage density, land use/land cover, rainfall and depth to groundwater level were used. The thematic layers and classes within them were given scale values based on literature and experts’ decision and calculated using Satty’s AHP. The thematic layers have been integrated via their weights/rates using weighted overlay spatial function tool of ArcGIS to provide GWP map. The result shows that GWP map comprises very good (13.4%), good (7%), moderate (23.6 %), poor (35.4%) and very poor (20.5%) zones. Validation of the GWP map with existing water point yields shows 84.21 % agreement indicating good accuracy of the method. The map removal sensitivity analysis result reveals that GWP is more sensitive to lithology (mean variation index, 1.92 %) and less sensitive to geomorphology (mean variation index, 0.59 %). Similarly, from the single layer sensitivity analysis, lithology and slope are found to be more effective parameters, whereas rainfall and depth to groundwater level are less effective variables.

Estimating seepage intensities from groundwater level time series by inverse modelling: A sensitivity analysis on wet meadow scenarios

2010 ◽  
Vol 385 (1-4) ◽  
pp. 132-142 ◽  
Author(s):  
Dirk Gijsbert Cirkel ◽  
Jan-Philip M. Witte ◽  
Sjoerd E.A.T.M. van der Zee
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Groundwater Level ◽  
Inverse Modelling ◽  
Wet Meadow
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