1. Introduction
Poesen et al. [
1] shed light on the importance of gully erosion for the proper land management and evolution of the geomorphology. They were first to discuss the relevance of modeling to properly understand the nature and fate of gully and piping erosion. Another key step in the understanding of the evolution of gully and piping erosion was the contribution of Vincent Chaplot [
2] when he updated the importance of the topography and parent material (and soil types) to research gully and piping evolution. This is part of a dynamic experimental and modeling approach to badland, gully, and piping landscapes [
3,
4].
Soil is one of the essential natural resources. From the deforestation in the middle ages to the industrial revolution by anthropic factors, soil has been under several natural and man-made threats [
5]. Agriculture, grazing [
6], poor land management [
7], and biological factors such as water and wind erosion frequently damage the soil [
8,
9]. Soil is a relevant component of the Earth system as it regulates the hydrological, biochemical, and life cycles, making it a definitive element in the achievement of the Sustainable Development Goals of the United Nations and the Land Degradation Neutrality challenge [
10]. Piping erosion causes significant changes in the landscape and environmental degradation [
11]. Piping is a subsurface drainage system that is often caused by dissolution but can also result from animal burrowing and nesting, and, if formed on a slope, with the collapse of the tunnel roof, gullies develop and eventually lead to badland formation and expansion [
12,
13]. Although piping erosion is not as widespread as agriculture erosion [
10], it has recently become an environmental challenge and public issue in several communities worldwide [
3].
Piping generally occurs in formations with low permeability and high dissolution minerals. With the absorption of water by the formation in the wet season as well as the presence of elements such as sodium that influence the hydrolysis of the clay, the clay expands and, during dry periods, numerous cracks develop and allow the percolation of the surface runoff. It should be noted, however, that tunnels are formed upon crack development [
14]. Despite the simple physical structure of these erosional forms, their mechanism of formation is very complicated. Soil, physiographic, climatic, and biological factors all contribute to the development of piping erosion, and they have different degrees of importance in different areas and conditions [
15].
Piping erosion results from the concentration of surface runoff and its uneven infiltration into the soil, the existence of a lower horizon with less permeability, and the creation of a hydraulic gradient [
16]. Researchers have not been able to determine precisely which of these factors has the highest contribution to the development of piping erosion due to the formation pipes in a wide range of soil textures, different climates, fauna, different land use classes, and wide slopes [
17,
18,
19,
20]. Piping erosion has led to unusual changes in the landscape, ultimately leading to inadequate agricultural practices and the inefficient use of natural resources [
12,
21]. Therefore, to control this type of erosion, it is best to take protective measures before it begins. The formation and onset of piping erosion is influenced by environmental factors. To design and implement soil conservation decisions and strategies, it is important to identify areas that are susceptible to piping erosion and the likelihood of piping erosion to begin and to take appropriate precautions in each area [
22].
In areas where the lithological and soil structure is composed of fine and discrete elements, the mechanism of the formation of piping erosion is such that this erosion causes washing and removal of fine particles by mechanical leaching and creates the conditions for tunnel erosion problems. Moreover, in areas with high solutes, water infiltration and solute dissolution accelerate the formation of piping erosion forms, or, in other words, dissolution plays a supportive role in creating these forms [
23,
24].
The climatic conditions of Iran and inadequate land management has led to accelerated land degradation, which has recently resulted in high soil erosion rates and sediment yield [
25]. Iran shows active soil erosion rates due to the land uses [
26], and climate and land management are the key factors [
27]. Many authors focused on the evaluation of the soil erosion rates to quantify them [
28]. The soil erosion studies in Iran focused on different factors and scales, from watershed scale soil erosion behavior [
29] till the control that some factors such as texture exert [
30], to gully erosion [
31], soil detachment [
32], or the use of the Universal Soil Loss Equation [
33]. Although the research on soil erosion in Iran is a key environmental issue, piping erosion is a topic which was not addressed until recently [
24] and literature about it is scarce. Although piping is relevant in Iran and other semiarid lands in the world [
14,
34,
35], there is a need to deeply understand the spatial distribution, the origin and development, and the proper management in order to prevent it. Mapping piping and researching the control that different factors exert on piping development is an ongoing challenge for today’s scientists, as Bernatek-Jakiel and Poesen [
14] highlighted in their article on the current state of the art in this field.
All the above-mentioned constraints in the scientific research of piping can be addressed with machine learning models, which are already widely used in predicting various phenomena such as gully, landslide, and land subsidence [
31,
36]. Machine learning models have the excellent ability to identify the occurrence behavior of phenomena in terms of the use of distributed estimation algorithms, data-driven nature, and high iteration of the modeling process. In numerous investigations, different machine learning models have been used to model zoning erosion phenomena, such as support vector machine (SVM) [
37,
38,
39,
40], random forest (RF) [
31,
41,
42,
43,
44,
45,
46], naive Bayes [
42,
47], multivariate adaptive regression splines (MARS) [
41,
48,
49], artificial neural network (ANN) [
50], and logistic regression (LR) [
51,
52,
53].
The scientific community is looking for proper and comprehensive methods to create susceptibility maps. For this, we need pilot areas (representative watersheds and slopes) on which to test the new strategies to forecast piping development. Various studies have been carried out on the use of a machine learning method in mapping sensitivity and risk phenomena [
54]. In the study area, despite the extensive expansion of piping erosion and the susceptibility of the area to surface wash, no research has been conducted to determine the potential of piping erosion and the factors that cause piping formation and development. Therefore, in this study, we evaluate the factors affecting piping erosion and finally piping erosion susceptibility mapping. In this study, the performance of random forest (RF), support vector machine (SVM), and generalized linear Bayesian models (GLM Bayesian) in piping erosion hazard susceptibility using Advance Land Observatin Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) digital elevation model (DEM) was evaluated.