Erosion potential mapping using analytical hierarchy process (AHP) and fractal dimension

Abstract

Assessing landform vulnerability to soil erosion is crucial for improved sustainable land use planning and management. In the Loess Plateau of the Northern Shaanxi Province of China, soil erosion has been reported as a major threat to sustainable land management and impacts on driving the socio-economic benefits that can be accrued from the landforms. Several studies especially on Erosion Potential Mapping (EPM) in the region have been conducted but the role of the fractal dimension (FD) of the terrain features has been limited. In this study, the paper assessed the role of fractal terrain features on the overall EPM. The Analytical Hierarchical Process (AHP) was adopted using 6 criteria, FD of the terrain, Land Use Land Cover, Slope, Elevation, Geomorphology and Flow Accumulation. These were developed in a Geographic Information System (GIS) framework. Eight Scales (8) were evaluated in order to select the best Scale with the lowest Consistency Ratio (CR) and the Minimum Relative Error (MRE). The results from this study shows that fractal features of terrain when integrated with the rest of the criteria produced a reliable EPM for the study area. The absence of the FD also gives unrealistic results for the EPM. The EPM with FD distribution recorded 29.4% for low erosion potential whereas EPM without FD recorded 46.7%. A larger portion of the Shaanxi province (70%) is found to be at a higher risk of erosion. Therefore, it is hoped that the findings from this research would further boost the integration of fractals into EPM in China and similar regions across the World. The study further recommends that sustainable soil management measures are put in place to reduce the erosion risk in the province to protect the natural ecological habitat.

Modelling the causes of boiler accidents: implications for economic and social sustainability at the workplace

“…Analytic hierarchy process (AHP), a very popular type of multi-criteria decision making (MCDM) methods, was introduced by Thomas Saaty (1990) . This method has widely been used in complex decision-making problems in the area of safety and risk analysis ( Li et al., 2020 ; Kabo-bah et al., 2021 ), maintenance management ( Pagano et al., 2021 ), supply chain management ( Mastrocinque et al., 2020 ), project management ( Büyüközkan et al., 2021 ) etc. The main advantage of this method is that the alternatives for the complex decision-making process are analyzed by the experts from the mathematical and psychological point of view.…”

An Alternative Process for Determining Erosion Risk: The Fuzzy Method

“…A pairwise comparison matrix is obtained based on the pairwise comparisons between the criteria considered suitable for use in a study, and then the weight values of these criteria are determined (Kazakis et al, 2015). AHP not only involves physical and planning factors such as landslides, earthquakes, erosion, and flooding (Boroumandi et al, 2015;Shadmaan & Ibne Islam, 2021;Wei et al, 2020;Kabo-bah et al, 2021) from education to logistics, the manufacturing industry, and health sector (Şahin & Yurdugül, 2018;Çakılcı & Öztürkoğlu, 2020;Sarjono et al, 2020;Schmidt et al, 2016), but ıt is also used extensively in many other subjects. Saaty (1987, p. 161) stated AHP to have been developed as a method that can be used in multi-criteria decision-making not only in the physical environment but also in social areas.…”

“…For these purposes, geographical information systems (GISs) and remote sensing methods (Mitasova et al, 2013;Ganasri & Ramesh, 2016;Jabbar, 2003;Parlak, 2010;Chowdhury & Tripathi, 2013;Arabameri et al, 2018) have been prosperous in studies carried out on erosion in different continents of the world. The analytic hierarchy process (AHP) is one of the multi-criteria decision making techniques that uses parameters such as slope, precipitation, lithological structure, and vegetation and, with its proven reliability (Vulević et al, 2015;Chakraborty et al, 2016;Abuzaid et al, 2021;Belloula et al, 2020;Tairi et al, 2019;Kabo-bah et al, 2021), has been used extensively in recent years. It is one of the most reliable frequently used methods, especially for producing erosion risk maps.…”

Forecasting Soil Erosion Risk Using GIS and Remote Sensing for the Nam Un Basin, Sakon Nakhon Province, Thailand

“…RI is the averaged capable of functioning resulting from randomly generated comparisons; it changes based basin protection and human actions A by Wischmeier and Smith is computed by RUSLE [13]. A = R × K × LS × C × P (7) where A is the computed spatial average soil loss and temporal average soil loss (in t ha -1 year -1 ), R is the rainfall-runoff erosivity factor (MJ mm ha -1 h -1 year -1 ), K is the soil erodibility factor (t h -1 MJ -1 mm), LS is the slope-length and slope steepness factor (-), C is the cover management factor (-), and P is the support [14][15][16][17][18][19][20]. Practice factor (-) In order to compute the geographically distributed soil loss in the Nam UN Basin, the input variables of the RUSLE model Equation ( 7) were included into ArcGIS 10.5.…”

“…Practice factor (-) In order to compute the geographically distributed soil loss in the Nam UN Basin, the input variables of the RUSLE model Equation ( 7) were included into ArcGIS 10.5. Water bodies within the Nam UN Basin were omitted from the calculation of A since the mechanism of soil erosion and delivery differs substantially between land and water [20,21].…”