Determining the features influencing the-S soil quality index in a semiarid region of Iran using a hybrid GA-ANN algorithm

Abstract S index as a soil physical quality is the slope of the soil water release curve on a mass base at its inflection point on a logarithmic matric potential scale. It is necessary to select the most influential soil properties on S index to predict it. In the current study, to select properties that influence S index, a hybrid algorithm: genetic algorithm (GA) in combination with an artificial neural network (ANN) was designed. The potential power of the GA-ANN algorithm in setting up a framework for identifying the most determinant parameters of S index in an Iranian semiarid region was also investigated. Results of GA-ANN algorithm for selecting best dataset showed that five properties including soil organic matter (SOM), sand, clay, carbonate calcium equivalent (CCE), and bulk density (BD) had the lowest error. The ANN method resulted in a higher model efficiency (R2 = 0.92, RMSE = 0.00065 and MAPE = 0.14% than MLR (multiple linear regression (approach (R2 = 0.0.016, RMSE = 2.72 and MAPE = 43. 16%). Results of sensitivity analysis for the ANN model showed that BD and CCE had the highest and the lowest effects on S index, respectively. Considering the sensitivity analysis results and also that fact that measurement of the S index is not cost-efficient, it is suggested that bulk density be used as an indicator of soil quality.