The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches

Abstract

In this study, as a result of the determination of the Least limiting water range (LLWR) contents of alluvial lands with different soils, which are distributed in the Bafra Plain where intensive agricultural activities are carried out, the compression and aeration problems in the area are revealed with distribution maps. In addition, the predictability of LLWR was evaluated with the random forest (RF) algorithm, which is one of the machine learning algorithms, and the usability of the prediction values distribution maps was revealed. The LLWR contents of the soils varied in the range of 0.049- 0.273 cm3 cm-3 for surface soils. While there were aeration problems in 6.72%, compaction problems in 20.16%, both aeration and compaction problems in 0.8% of the surface soils examined in the study area, 72.32% were determined under optimal conditions. For the 20-40 cm depth, an aeration problem in the 5.88%, a compaction problem in the 28.57%, and both an aeration and a compaction problem in the 2.52% of the points were detected. In the estimation of LLWR with the random forest algorithm, the RMSE value obtained for 0-20 cm depth was determined as 0.0218 cm3 cm-3, and for 20-40 cm it was determined as 0.0247 cm3 cm-3 . In the distribution maps of the observed and predicted values obtained, the lowest RMSE value was determined by Simple Kriging interpolation methods for 0-20 cm depth and Ordinary Kriging interpolation methods for 20-40 cm. While the distribution of obtained and predicted values in surface soils was similar, variations were found in the distribution of areas with low LLWR below the surface. As a result of the study, it has been revealed that LLWR can be obtained with a low error rate with the RF algorithm, and distribution maps can be created with lower error in surface soils.