Evaluation of Best First Decision Tree on Categorical Soil Survey Data for Land Capability Classification

Abstract

Land capability classification (LCC) of a soil map unit is sought for sustainable use, management and conservation practices. High speed, high precision and simple generating of rules by machine learning algorithms can be utilized to construct pre-defined rules for LCC of soil map units in developing decision support systems for land use planning of an area. Decision tree (DT) is one of the most popular classification algorithms currently in machine learning and data mining. Generation of Best First Tree (BF Tree) from qualitative soil survey data for LCC reported in reconnaissance soil survey data of Wardha district, Maharashtra has been demonstrated in the present study with soil depth, slope, and erosion as attributes for LCC. A 10-fold cross validation provided accuracy of 100%. The results indicated that BF Tree algorithms had good potential in automation of LCC of soil survey data, which in turn, will help to develop decision support system to suggest suitable land use system and soil and water conservation practices.