Prediction of Soil Enzymes Activity by Digital Terrain Analysis: Comparing Artificial Neural Network and Multiple Linear Regression Models

2012 ◽  
Vol 29 (8) ◽  
pp. 798-806 ◽  
Author(s):  
Samaneh Tajik ◽  
Shamsollah Ayoubi ◽  
Farshid Nourbakhsh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Enzymes ◽  
Regression Models ◽  
Terrain Analysis ◽  
Linear Regression Models ◽  
Digital Terrain Analysis ◽  
Digital Terrain ◽  
Multiple Linear Regression Models ◽  
Soil Enzymes Activity

scholarly journals Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

2017 ◽  
Vol 44 (12) ◽  
pp. 994-1004 ◽  
Author(s):  
Ivica Androjić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Linear Regression Models ◽  
Artificial Neural ◽  
Multiple Linear Regression Models

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

scholarly journals Artificial Neural Network for analyzing the chaotic time series motion: The case of the Lebanese GDP

2021 ◽  
Author(s):  
Jean-François Verne
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Phase Space ◽  
Regression Models ◽  
Chaotic Dynamic ◽  
Linear Regression Models ◽  
Large Fluctuations ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract In this paper, we propose to analyze the motion of the Lebanese GDP over the period 1950-2019. This macroeconomic aggregate reveals large fluctuations notably during the civil war period (1975-1990). By estimating the Lyapunov exponents with the Artificial Neural Network (ANN) procedure, we show that this series exhibits a strange attractor generated by a chaotic dynamic and we use the embedding procedure to shed in light the bizarre structure of such a series. Thus, the ANN method gives better results regarding prediction than other linear regression models and allows to fit with accuracy the chaotic motion followed by the Lebanese GDP in the phase space.

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