Simple methods for estimating field capacity using Mamdani inference system and regression tree

Abstract

Using easily measurable soil properties could save time and cost for field capacity (FC) prediction. The objective of this study was to compare Mamdani fuzzy inference system (MFIS) and regression tree (RT) for FC predicting using such properties. One hundred and sixty-five soil samples from Unsaturated Soil hydraulic database data-set and 45 from Hydraulic Properties of European Soils data-set were used for the development and validation of MFIS and RT, respectively. Fuzzy rules and tree diagram based on the relationships between these predictor variables and the response variable FC were defined and 48 rules were written. Results showed a positive linear relevancy in terms of standardized independent variable weight, W*, between clay content and FC and negative linear relevancy between geometric mean particular size diameter (d_g) and FC. Among predictor variables, d_g (W* = 0.81) and bulk density (BD) (W* = 0.49) had the highest and lowest influence on FC, respectively. A tree diagram is presented for the prediction of FC using clay content, dg, and BD. Overall, based on statistical parameters, RT method (R² = 0.78, geometric mean error (GME) = 1.02, mean error (ME) = 0.01 cm³ cm⁻³, and root mean square error (RMSE) = 0.04 cm³ cm⁻³) showed a higher performance than MFIS method (R² = 0.72, GME = 1.16, ME = 0.08 cm³ cm⁻³, and RMSE = 0.06 cm³ cm⁻³) to predict FC.

Keywords:

• regression tree
• Mamdani fuzzy inference system
• field capacity
• UNSODA
• HYPRES

Acknowledgement

I would like to acknowledge the assistance of Dr Allan Lilly (James Hutton Institute, Scotland) in providing data from the HYPRES database.