Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives

Abstract

Soil bulk density (BD) is very important factor in land drainage and reclamation, irrigation scheduling (for estimating the soil volumetric water content), and assessing soil carbon and nutrient stock as well as determining the pollutant mass balance in soils. Numerous pedotransfer functions have been suggested so far to relate the soil BD values to soil parameters (e.g. soil separates, carbon content, etc). The present paper aims at simulating soil BD using easily measured soil variables through heuristic gene expression programming (GEP), neural networks (NN), random forest (RF), support vector machine (SVM), and boosted regression trees (BT) techniques. The statistical Gamma test was utilized to identify the most influential soil parameters on BD. The applied models were assessed through k-fold testing where all the available data patterns were involved in the both training and testing stages, which provide an accurate assessment of the models accuracy. Some existing pedotransfer functions were also applied and compared with the heuristic models. The obtained results revealed that the heuristic GEP model outperformed the other applied models globally and per test stage. Nevertheless, the performance accuracy of the applied heuristic models was much better than those of the applied pedotransfer functions. Using k-fold testing provides a more-in-detail judgment of the models.