Prediction effect of farmyard manure, multiple passes and moisture content on clay soil compaction using adaptive neuro-fuzzy inference system

Abstract Soil compaction by machine traffic is a complex process with many interacting factors. The strength of adaptive neuro-fuzzy inference system (ANFIS) is the ability to handle linguistic concepts and find nonlinear relationships between inputs and outputs parameters. In this research, the effect of farmyard manure, the number of tire passes, soil moisture contents and three average depths on clay soil compaction is predicted using ANFIS and Regression. For the prediction of soil compaction, an agricultural tractor tire was used and the experiments were carried out in the controlled condition of soil bin facility utilizing a well-equipped single-wheel tester. To measure soil compaction, cylindrical cores in groups of three were inserted into the three different depths. Various member function ANFIS were tested to discover the supervised ANFIS-based models for the soil compaction. On the basis of statistical performance criteria of MAPE and R 2 , Gaussian curve built-in membership function (gaussmf) was found as a proper model. In addition, the ANFIS model with ‘Gaussian mf’ is recommended considering the higher prediction performance values of MAPE = 0.2957%. The regression analyses of ANFI and Multiple Linear Regression (MLR) revealed a high correlation with farmyard manure, the number of tire passes, soil moisture, and depth. Also, it showed a higher performance compared to the regression model for predicting soil compaction. Thus, it can be concluded that ANFIS-based methodology is a soft computing approach that provides excellent nonlinear systems such as soil compaction.

[1]  Hamid Taghavifar,et al.  Prognostication of vertical stress transmission in soil profile by adaptive neuro-fuzzy inference system based modeling approach , 2014 .

[2]  Tim Davis Geotechnical Testing, Observation, and Documentation , 2001 .

[3]  Ahmed El-Shafie,et al.  A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam , 2007 .

[4]  Indra Mani,et al.  Effect of multiple passes of tractor with varying normal load on subsoil compaction , 2011 .

[5]  K. P. Sudheer,et al.  Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models , 2008 .

[6]  Kazım Çarman,et al.  Prediction of soil compaction under pneumatic tires a using fuzzy logic approach , 2008 .

[7]  Shahaboddin Shamshirband,et al.  Using ANFIS for selection of more relevant parameters to predict dew point temperature , 2016 .

[8]  Amit K. Verma,et al.  A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass , 2005 .

[9]  Iman Nasiri Aghdam,et al.  A new hybrid model using Step-wise Weight Assessment Ratio Analysis (SWARA) technique and Adaptive Neuro-fuzzy Inference System (ANFIS) for regional landslide hazard assessment in Iran , 2015 .

[10]  Ahmet Tortum,et al.  Prediction of the unconfined compressive strength of compacted granular soils by using inference systems , 2009 .

[11]  Oguz Kaynar,et al.  Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils , 2010, Expert Syst. Appl..

[12]  B. D. Soane,et al.  The role of organic matter in soil compactibility: a review of some practical aspects. , 1990 .

[13]  R. J. Stone,et al.  Organic Matter Effects on the Strength Properties of Compacted Agricultural Soils , 1995 .

[14]  Pejman Tahmasebi,et al.  Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran , 2010 .

[15]  Robert L. Blevins,et al.  THE EFFECTS OF ORGANIC MATTER AND TILLAGE ON MAXIMUM COMPACTABILITY OF SOILS USING THE PROCTOR TEST1 , 1996 .

[16]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  B. Kay,et al.  Sensitivity of soil structure to changes in organic carbon content: Predictions using pedotransfer functions , 1997 .

[19]  Donald C. Erbach,et al.  SOIL STRAIN UNDER THREE TRACTOR CONFIGURATIONS , 1992 .

[20]  M. Alvarez Grima,et al.  Modeling tunnel boring machine performance by neuro-fuzzy methods , 2000 .

[21]  H. Rogers,et al.  Characterizing root distribution with adaptive neuro-fuzzy analysis , 2011 .

[22]  Alex J. Cannon,et al.  Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models , 2002 .

[23]  W. Anderson,et al.  Soil compaction in cropping systems: A review of the nature, causes and possible solutions , 2005 .

[24]  A. Mardani,et al.  Studies on a long soil bin for soil-tool interaction. , 2010 .

[25]  Momcilo Markus,et al.  PRECIPITATION-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS AND CONCEPTUAL MODELS , 2000 .

[26]  Vilas M. Salokhe,et al.  Modeling compaction in agricultural soils , 2002 .

[27]  D. Fallow,et al.  Susceptibility to compaction, load support capacity, and soil compressibility of Hapludox , 2004 .

[28]  I. Angin,et al.  Effects of Vermicompost Application on Soil Aggregation and Certain Physical Properties , 2016 .

[29]  Mohammad Reza Mosaddeghi,et al.  Soil compactibility as affected by soil moisture content and farmyard manure in central Iran , 2000 .