Estimation of Soil Compression Coefficient Using Artificial Neural Network and Multiple Regressions
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2013, Vol 4, Issue 10
Abstract
Measurement of some significant properties of soil might be difficult, costly and timeconsuming. Thus, estimation of these characteristics using conveniently measurable soil properties may be useful. In this research, it is attempted to evaluate and examine the artificial neural network technique and multiple regression in order to measure the soil compression coefficient using conveniently measurable soil properties. A total of 100 soil samples were taken randomly from various areas of Ahwaz and the percentage of clay, silt, sand, wet bulk density, dry bulk density, friction coefficient, viscosity coefficient, plastic limit were determined as conveniently measurable properties (dependent variable) and the compression coefficient as costly measured properties ( independent variable).In order to form Annual Neural Network, instructional algorithm Mark- Laurinburg and Perseptron stricter was use and the stepwise method was used in order to make regression transfer functions. The compression coefficients of soils were mean 0.16, at least 0.11 and at most 0.25 and depended on clay soil class. The results showed that the compression coefficient r=0.63 and MSE=0.006 was determined by neural network method. In the regression method, it was measured as r=0.47 and MSE=0.002. By comparing the values of correlation coefficient and error square mean by two using methods, it was revealed that artificial neural network has the least error and the most accuracy. Therefore in the study area the practice of this method is recommended for estimating the compression coefficient.
Authors and Affiliations
Farzaneh Namdarvand *| Department of Soil Science, Science and Research Branch, Islamic Azad University, Khouzestan, Iran, farzaneh.namdar@gmail.com, Alireza Jafarnejadi| Soil and Water Research Department of Agricultural and Natural Resources Research Center of Khouzestan, Iran, Gholamabbas Sayyad| Department of Soil Science, Shahid Chamran University, Ahwaz, Iran
Keywords
Related Articles
- EP ID EP5742
- DOI -
- Views 283
- Downloads 8
Study of the relationship between knowledge and emotional intelligence; case study: Melli bank of Qom
In recent years, knowledge management has been known as one of the most interesting and challenging subjects in business management and its range of application is increasing along with other subjects of management. Emot...
The Relationship between Forgiveness and Attribution Style in Addiction-Prone and Non-Prone People Individuals
This study attempts to examine the relation of forgiveness, and attribution style in addiction prone and non-prone individuals. Population is all undergraduate students of Azad university of Zahedan over 2013-2014. Sampl...
Bacterial Foraging Algorithm to Find Optimal Cluster Head in Sensor Network
Recently, different methods have been developed for smart calculations and Metaheuristics. The present study introduces bacterial foraging methods to find optimal cluster-head in a sensor network. Considering a sensor as...
Using Arima Time Series Model in Forecasting the Trend of Changes in Qualitative Parameters of Sefidrud River
Sefidrud River is considered as the biggest river in northern Iran which is undergoing many changes due to increased water needs in agricultural, industrial and other sectors as well as increased pollution compiled by su...
Comparing the Degree of Client's Satisfaction to the Performance of Branches and Agencies of Social Security
Social security organization as the biggest insurance organization in Iran is the only full suppler of social support and attracting clients' satisfaction is the secret of permanency and fundamental elements of the organ...