Machine learning approaches for the prediction of soil aggregate stability

Abstract

Currently, many Pedotransfer Functions (PTFs) are being developed to predict certain soil properties worldwide, especially for difficult and time-consuming parameters to measure. However, very few studies have been done to assess the feasibility of using PTFs (regression or machine learning methods) for predicting soil aggregate stability. Also, the Random Forest (RF) method has never been used before to predict this parameter, and no study was found concerning the use of PTFs methods to estimate soil parameters in Morocco. Therefore, the current study was conducted in the three watersheds of Settat-Ben Ahmed Plateau, located in the center of Morocco and covering approximately 1000 km 2. The purpose of this study is to compare the capabilities of the machine learning technique (Random Forest) and Multiple Linear Regression (MLR) to predict the Mean Weight Diameter (MWD) as an index of soil aggregate stability using soil properties from two sources data sets and remote sensing data. The performance of the models was evaluated using a 10-fold cross-validation procedure. The results achieved were acceptable in predicting soil aggregate stability and similar for both models. Thus, the addition of remote sensing indices to soil properties does not improve models. Results also show that organic matter is the most relevant variable for predicting soil aggregate stability for both models. The developed models can be used to predict the soil aggregate stability in this region and avoid waste of time and money deployed for analyses. However, we recommend using the largest and most uniform possible data set to achieve more accurate results.

Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

“…Many previous studies can also support these results (Besalatpour et al, 2014;Shi et al, 2020;Jones et al, 2021;Kamamia et al, 2021). In contrast, Bouslihim et al (2021) reported a low contribution of remote sensing indices in estimating SAS, and this was justified by the presence of other parameters that often reduced the role of remote sensing indices. Although some studies have reported the role of climate in SAS distribution (Cerdà, 2000;Guan et al, 2018;Le Bissonnais et al, 2018), these data were not used in the current paper due to the small size of the study area and the limited spatial change for climatic conditions.…”

“…What is interesting is the absence of very unstable soils throughout the region (MWD < 0.4 mm) with a domination of the two classes: moderately stable (0.8 < MWD < 1.3 mm) and stable (1.3 < MWD < 2.0 mm). This can be underpinned by the findings by Bouslihim (2020), Bouslihim et al (2021) regarding the low soil erosion rates in the region; moreover, the only exposed area to erosion is the downstream part of the El Himer watershed due to the factors mentioned earlier (anthropogenic factors and topography).…”

“…In remote sensing, the variation of the spectral response measured at the satellite sensor is an indicator of environmental change. For the quantification of vegetation cover, several vegetation indices can be used (Bouslihim et al, 2021). In the literature, the normalized difference vegetation index (NDVI) (Figure 2E) is the most popular and widely used (Mahmoudabadi et al, 2017;Maynard and Levi, 2017;Lamichhane et al, 2019).…”

“…Overall, this technique achieves its effectiveness due to the availability of many important factors, such as the significant progress of machine learning algorithms and their widespread applications in several fields (Wadoux et al, 2020), including soil science, and their contribution to the prediction and mapping of different continuous soil propreties such as organic carbon (Lamichhane et al, 2019), soil plasticity (Al Masmoudi et al, 2021), soil aggregate stability (Rivera and Bonilla, 2020;Bouslihim et al, 2021) and texture (Barman and Choudhury, 2020). It is also involved in the prediction of discontinuous soil characteristics such as soil classes and soil horizons (Zeraatpisheh et al, 2020).…”

Wet aggregate stability modeling based on support vector machine in multiuse soils

“…The experimental results show that the prediction capability of SVM is better than that of the MLR and ANN models with the same number of input parameters and data points. Y Bouslihim et al 19 propose ML approaches to predict SAS. In the study, they compared the ability of RF and MLR to predict MWD as an SAS index.…”