Section 7 update: Multivariate statistical methods and artificial neural networks for analysis of microbial community molecular fingerprints

2008 ◽  
pp. 3349-3387
Author(s):  
Sherry Dollhopf ◽  
HÉctor Ayala-Del-RÍo ◽  
Syed Hashsham ◽  
James Tiedje
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Microbial Community ◽  
Statistical Methods ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Molecular Fingerprints ◽  
Artificial Neural

Prediction of Internal Stability for Geogrid-Reinforced Segmental Walls

2010 ◽  
Vol 163-167 ◽  
pp. 1854-1857
Author(s):  
Anuar Kasa ◽  
Zamri Chik ◽  
Taha Mohd Raihan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Statistical Methods ◽  
Retaining Wall ◽  
Retaining Walls ◽  
Internal Stability ◽  
Residual Soil ◽  
Concrete Masonry ◽  
Artificial Neural

Prediction of internal stability for segmental retaining walls reinforced with geogrid and backfilled with residual soil was carried out using statistical methods and artificial neural networks (ANN). Prediction was based on data obtained from 234 segmental retaining wall designs using procedures developed by the National Concrete Masonry Association (NCMA). The study showed that prediction made using ANN was generally more accurate to the target compared with statistical methods using mathematical models of linear, pure quadratic, full quadratic and interactions.

scholarly journals Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1548
Author(s):  
Edyta Puskarczyk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Thin Layers ◽  
Well Logs ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Artificial Neural ◽  
Detailed Interpretation ◽  
Group B

The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.

The significance of relevance trees in the identification of artificial neural networks input vectors

2013 ◽  
Vol 24 (1) ◽  
pp. 27-34
Author(s):  
G. Manuel ◽  
J.H.C. Pretorius
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Methods ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Wide Range ◽  
Accurate Forecast ◽  
Artificial Neural

In the 1980s a renewed interest in artificial neural networks (ANN) has led to a wide range of applications which included demand forecasting. ANN demand forecasting algorithms were found to be preferable over parametric or also referred to as statistical based techniques. For an ANN demand forecasting algorithm, the demand may be stochastic or deterministic, linear or nonlinear. Comparative studies conducted on the two broad streams of demand forecasting methodologies, namely artificial intelligence methods and statistical methods has revealed that AI methods tend to hide the complexities of correlation analysis. In parametric methods, correlation is found by means of sometimes difficult and rigorous mathematics. Most statistical methods extract and correlate various demand elements which are usually broadly classed into weather and non-weather variables. Several models account for noise and random factors and suggest optimization techniques specific to certain model parameters. However, for an ANN algorithm, the identification of input and output vectors is critical. Predicting the future demand is conducted by observing previous demand values and how underlying factors influence the overall demand. Trend analyses are conducted on these influential variables and a medium and long term forecast model is derived. In order to perform an accurate forecast, the changes in the demand have to be defined in terms of how these input vectors correlate to the final demand. The elements of the input vectors have to be identifiable and quantifiable. This paper proposes a method known as relevance trees to identify critical elements of the input vector. The case study is of a rapid railway operator, namely the Gautrain.

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