Evaluation of Linear and Machine Learning Models for Determining Pedotransfer Functions

Abstract

Modelling of water content and the transport of water in soil has become a useful tool in simulating agricultural productivity or in solving various hydrological analyses. For instance, optimum irrigation management requires a systematic estimation of the soil-moisture to determine both the appropriate amount and timing of irrigation. Soil characteristics appear as an essential input in the numerical simulation of a soil-water regime. A critical physical property used in the description of a soil-water regime in such modelling is a soil water retention curve. This paper aims to evaluate so-called pedotransfer functions, which helps to assess the soil water retention curve easier than by standard complex and lengthy procedure involving both field and laboratory work. As a case study Zahorska Lowland, which is located in central Europe in the western part of Slovakia, was selected. The frequent occurrence of dry years in this area results in the necessity to construct irrigation systems in this area, so modelling water content in the soil is an important task here. This study aims to support such modelling with determining pedotransfer functions. Authors compare linear methods (multiple linear regression, LASSO regularized regression) and CatBoost machine learning model.