Identifying the Determinant Factors Influencing S Index in Calcereous Soils Using Anneling Simulated– Artificial Neural Network Hybrid Algorithm

Abstract

Use of the curve gradient of the Soil Water Retention Curves (SWRC) in the inflection point (S Index) is one of the main indices for assessing the soil quality for management objectives in agricultural and garden lands. In this study Anneling Simulated – artificial neural network (SA-ANN) hybrid algorithm was used to identify the most effective soil features on estimation of S Index in Jiroft plain. For this purpose, 350 disturbed and undisturbed soils samples were collected from the agricultural and garden lands and then some physical and chemical soil properties including Sand, Silt, Clay percent, Electrical Conductivity at saturation, Bulk Density, total porosity, Organic Mater, and percent of equal Calcium Carbonate were measured. Moreover, the soil moisture amount was determined within the suctions of 0, 10, 30, 50, 100, 300, 500, 1000, 1500 KP using pressure plate. Then, the determinant features influencing the modeling of S Index were derived using SA-ANN hybrid algorithm. The results indicated that modeling precision increased by reducing the input variables. According to the sensitivity analysis, the Bulk Density had the highest sensitivity coefficient (sensitivity coefficient=0.5) and was identified as the determinant feature for modeling the S Index. So, since increasing the number of features does not necessarily increase the accuracy of modeling, reducing input features is due to cost reduction and time-consuming research.

Keywords

• soil physical quality
• feature selection
• sensitivity analysis
• meta-heuristic algorithms