remote sensing
Article Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model
Alireza Arabameri 1,* , John P. Tiefenbacher 2 , Thomas Blaschke 3 , Biswajeet Pradhan 4,5 and Dieu Tien Bui 6,*
1 Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran 2 Department of Geography, Texas State University, San Marcos, TX 78666, USA; [email protected] 3 Department of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, Austria; [email protected] 4 Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, Faculty of Engineering and IT, University of Technology Sydney, Sydney, 2007 NSW, Australia; [email protected] 5 Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea 6 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam * Correspondence: [email protected] (A.A.); [email protected] (D.T.B.)
Received: 17 January 2020; Accepted: 2 March 2020; Published: 9 March 2020
Abstract: The morphometric characteristics of the Kalvar¯ ¯ı basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvar¯ ¯ı drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, 3 2 1 which indicated that sub-basins 1 and 4, with mean sediment yields of 943.7 and 456.3 m km− year− , respectively, have the highest and lowest SE susceptibility in the study area. The sensitivity analysis revealed that the most sensitive parameters of the MPSIAC model are slope (R2 = 0.96), followed by runoff (R2 = 0.95). The MPSIAC shows that the ensemble model has a high prediction accuracy. The method tested here has been shown to be an effective tool to improve sustainable soil management.
Keywords: soil erosion; drainage network; morphometry; novel ensemble technique; Kalvar¯ ¯ı Basin
Remote Sens. 2020, 12, 874; doi:10.3390/rs12050874 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 874 2 of 24
1. Introduction Soil erosion (SE) threatens sustainable development [1]. More than three hundred years is needed to form a centimeter of soil [2], therefore the prevention of SE is vital to protect valuable resources [3]. In arid and semiarid regions, SE destroys soil fertility and threatens agriculture [4]. Defoliation, flooding, reduced effectiveness of dams, increasing economic losses, desertification, and land use changes are major consequences of SE [5]. The global rate of annual SE is approximated to be 75 billion tons [6]. Iran loses more than two billion tons every year, approximately three times the rates of other Asian countries. Approximately 125 million of the 165 million ha of the country experiences erosion [7]. This is roughly 75% of the entire country and 60% of Iran’s agricultural land. Iran’s average erosion is 30–32 ton/ha/year, which is 4.3 times the global average [7]. Economic losses caused by SE in Iran have been estimated to be 10 trillion rial [8]. Therefore, SE management and protection in Iran is urgently needed, in line with the United Nations Sustainable Development Goals [9]. This study prepared a SE risk map using effective factors to predict locations that are more likely to be SE “hot spots.” A basin is the developmental unit used to effectively manage resources sustainably [10]. It is a natural hydrological feature within which runoff is directed into collecting channels, streams, or rivers [11]. The characteristics and conditions of a basin or drainage basin determine the development of the landforms within it. Therefore, identification of the most important characteristics of a basin is integral to understanding its geomorphology [12]. Basin management planning can strive to control losses from SE in a basin [13]. Although there are several factors that influence SE, the major agent is water. SE is not a new problem, but it has become common and is worsening in Iran. It is particularly visible in the Kalvar¯ ¯ı river basin. Soil, geomorphology, hydrology, and landscapes are intimately linked. Measurement and analysis of Earth’s surface through geomorphometry is undertaken to understand the relationships between the sizes and shapes of a region’s features [10]. SE, runoff, the evolution of rivers, sedimentation, and drainage geometry are reflections of hydrological and geomorphic processes, and are ultimately assessable using morphometry [10]. Therefore, the morphometry of a basin can explain its hydrological behaviors. The basin is the unit of morphometric analysis, which is a technique introduced by Horton [14,15] and elaborated upon by Strahler [16–18], Miller [19], and Schumm [20]. Together, they provided the foundations for the field of quantitative fluvial geomorphology [21]. Because resource development planning is generally conducted at the watershed scale, it is critical to prioritize management of natural resources for sustainable development [11]. Prioritization for soil or water conservation can be improved by quantitative analyses of morphometric watershed parameters [10]. Morphometric analysis quantitatively describes drainage systems to understand landform- and soil-development processes as well as erosional characteristics [22]. Morphometry is the quantitative measurement of the shapes and dimensions of Earth’s landforms. Areas, volumes, elevations, slopes, profiles, and textures of the surface are the characteristics of utmost interest [22]. River basin morphometry reflects the hydrological and geomorphic responses of runoff, SE, flooding, drought, sedimentation, fluvial bifurcation, the flow characteristics and flow volumes of drainages and rivers, and the capacities and sustainability of reservoirs within a basin. Conventional studies have explored the relationships between the properties of drainage networks and other influences like climate, relief, lithology, structure, and tectonics to interpret morphometrics [23–25]. The tectonics of river basins have been examined for links to the geomorphological processes that shape drainages [26]. Recently, stream network morphometry has been used more often to assess water, soil, and hazards in developing countries [27–30]. Geospatial analytical techniques (geographic information systems (GIS) and remote sensing (RS)), and multi-criteria decision-making models (MCDM) have enhanced quantitative assessments of drainage networks, thematic mapping of morphometry, and applied morphometric analyses [31–36]. Furthermore, other software designed to quantify and calculate linear, areal, shape, and relief MPs have also advanced the sophistication of morphometrics [37,38]. Comparisons of conventionally and manually collected data to automated geospatial methods have demonstrated that modern technologies Remote Sens. 2020, 12, 874 3 of 24 are powerful and cost-effective for better management and processing of data and for creating maps for a variety of applications [39,40]. Digital elevation models (DEMs) are a standard source of data that can be used to discern the mechanisms behind geomorphological processes. DEMs are developed using several data sources. Ground surveys, photogrammetry, optical RS, radar, and lidar are the principal data acquisition methods. Synthetic aperture radar (SAR) data are analyzed using interferometry and radargrammetry, which detect phase discrepancies in each pixel. Elevation change is determined stereoscopically [41]. Highly detailed models of elevations are developed by comparing precise measurements of phase-shifts of reflected wavelengths from Earth’s surface using interferometric synthetic aperture radar (InSAR) [42]. Numerous studies have examined the use of methods to analyze radar data to produce DEMs [41]. First-order streams (Horton’s “fingertip” streams) can be delineated precisely using DEMs. GIS and RS can be used to measure and calculate drainage-basin MPs economically and can enable efficient analysis of large amounts of spatial information [43–45]. Few studies have used morphometric analysis of SE to prioritize sub-watersheds. Altaf and Meraj [46] combined an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM with 14 MPs in a multi-criteria analysis (MCA) to assess the susceptibility of a watershed in the western Himalaya to SE. They found that three sub-watersheds could be regarded as very high priority (highly susceptible to SE) for management. Ahmad Rather et al. [47] coupled 13 morphometric, land use, and slope parameters and data from a Shuttle-Radar Topography Mission (SRTM) DEM in an MCA to estimate SE susceptibility in the Jhelum basin of Kashmir. Gajbhiye et al. [13] used an SRTM DEM and morphometric analysis on 14 sub-watersheds of the Manot River catchment (a tributary of the Narmada River) to discern SE susceptibility. One sub-watershed had the lowest compound parameter value and was therefore most likely to experience the greatest amount of SE. This study combines a novel MCDM-based approach (the complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP)) with MPs to prioritize sub-basins by their susceptibility to SE. SE was mapped: (1) to model non-quantitative parameters; (2) to devise a method to compare different types of variables and to handle data at different scales; (3) to eliminate strict assumptions behind the study; (4) to fill a gap in the scholarship wherein only a few studies have employed MCDM models to assess SE susceptibility; and (5) to undertake a comprehensive study of the capabilities of COPRAS-AHP ensemble models for SE susceptibility analysis. The study area is the Kalvar¯ ¯ı Basin in Iran, an area that experiences high rates of SE. The main objectives of this study were: (1) to analyze the MPs of the Kalvar¯ ¯ı Basin using GIS, RS, and a Phased-Array type L-band Synthetic Aperture Radar (PALSAR) DEM (spatial resolution 12.5 m); (2) to prioritize sub-basins and to identify the major MPs that influence SE in the Kalvar¯ ¯ı Basin using the COPRAS-AHP novel ensemble MCDM model; and (3) to identify the sub-basin landscape parameters that dictate the SE potential of fluvial forces and SE susceptibility throughout the region. The results will provide better SE management and will enhance other environmental activities like land use planning, hazard management, and water conservation. This approach to morphometric analysis could be transferred to other, similar highland basins in other arid regions.
2. Materials and Methods
2.1. Study Area The Kalvar¯ ¯ı Basin, with an area of 70.21 km2, is approximately 187 km southwest of Shahrekord in Chaharmahal va Bakhtiari Province, Iran. It is located between 31◦32033” and 31◦37031”N and 50◦29023” and 50◦380E (Figure1). The highest elevation is found in the western part of the basin at 1999 m above sea level (asl) and the lowest is in the southwestern part of the basin at 1100 m asl. Mean annual precipitation and temperature in the study area are 295 mm and 20.6◦C respectively [48]. The geology of the study area varies from Paleocene limestone, shale, and marl to Quaternary deposits, including low-level piedmont fan and valley-terrace deposits [49]. The Asmari Calcareous Formation, Remote Sens. 2020, 12, 874 4 of 24 theRemote Bakhtiari Sens. 2020 Clastic, 12, x FOR Formation, PEER REVIEW Quaternary sediments, and the Gachsaran Evaporite Formation4 of are 25 the most important formations in the study area [49]. The area’s surface is covered with rock outcrops andwith inceptisols rock outcrops [50]. and The inceptisols watershed [50] includes. The severalwatershed macro-faults, includes several formed macro mainly‐faults, by shale formed and phylitemainly contactby shale with and granitephylite andcontact re-crystallized with granite limestone and re‐crystallized formations. limestone The main formations. soil texture The is main silt loam soil coveringtexture is the silt hills.loam Runocoveringff height the hills. in the Runoff Kalvar¯ height¯ı Basin in is the 109.8 Kalv mm.ārī Basin The average is 109.8 slopemm. The of the average basin slope of the basin is 22.4˚. Land use/land cover classes in the study area include orchard (0.07%), low is 22.4◦. Land use/land cover classes in the study area include orchard (0.07%), low forest (23.05%), agriculture-dryforest (23.05%), farmingagriculture (0.58%),‐dry farming dry farming (0.58%), (31.06%), dry farming moderate (31. rangeland06%), moderate (0.009%), rangeland moderate (0.009%), forest (10.94%),moderate and forest poor (10.94%), rangeland and (34.27%). poor rangeland (34.27%).
Figure 1. Location of the study area in Iran. Figure 1. Location of the study area in Iran. 2.2. Methodology 2.2. Methodology There are seven steps to this method (Figure2), which are: (1) determine the boundaries of 11 sub-basinsThere usingare seven topographic steps to this maps method (1:50,000 (Figure scale 2), and which 20 m are: contours) (1) determine and PALSAR the boundaries DEM (12.5 of m11 resolution);sub‐basins using (2) extract topographic basic MPs maps (basin (1:50,000 area, basinscale length,and 20 numbersm contours) and and lengths PALSAR of streams DEM of(12.5 each m order,resolution); basin perimeter,2) extract basic andbifurcation MPs (basin ratio) area, of basi then Kalvlength,ar¯ ¯ı Basinnumbers from and drainage lengths networks, of streams sub-basin of each polygons,order, basin and perimeter, an elevation and bi modelfurcation generated ratio) of from the theKalv PALSARārī BasinDEM; from drainage (3) extract networks, linear, shape, sub‐basin and reliefpolygons, factors, and including an elevation mean model bifurcation generated ratio from (Rbm), the drainage PALSAR density DEM; (Dd),3) extract stream linear, frequency shape, (Fu),and texturerelief factors, ratio (T), including length of mean overland bifurcation flow (Lo), ratio infiltration (Rbm), drainage number density (If), constant (Dd), of stream channel frequency maintenance (Fu), (C),texture form ratio factor (T), (Rf), length shape factorof overland (Bs), elongation flow (Lo), ratio infiltration (Re), compactness number coe(If),ffi cientconstant (Cc), of circularity channel ratiomaintenance (Rc), ruggedness (C), form numberfactor (Rf), (Rn), shape basin factor relief (B (Bh),s), elongation relief ratio ratio (Rh), (Re), and slopecompactness (S) from coefficient the basic parameters(Cc), circularity andformula ratio (Rc), (Table ruggedness1); (4) apply number the AHP (Rn), model basin to relief determine (Bh), relief the relative ratio (Rh), importance and slope of the (S) morphometricfrom the basic factorsparameters in SE; and (5)calculate formula the(Table relative 1); 4) weight apply ofthe each AHP alternative model to (for determine the sub-basins) the relative and prioritizeimportance them of the using morphometric the COPRAS factors model; in (6) SE; prepare 5) calculate an SE susceptibility the relative weight map using of each the AHP-COPRASalternative (for ensemblethe sub‐basins) model; and and prioritize (7) validate them the using results the using COPRAS the MPSIAC model; model 6) prepare and non-parametric an SE susceptibility correlation map testsusing such the asAHP the‐ SpearmanCOPRAS ensemble correlation model; coefficient and test7) validate (SCCT) andthe results the Kendall using Tau the correlation MPSIAC model coefficient and testnon (KTCCT).‐parametric correlation tests such as the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). Morphometric (elevation and slope) and hydrologic (drainage networks) parameters were estimated using an ALOS PALSAR DEM (12.5 m resolution). Detailed descriptions of the methodology used to produce the ALOS PALSAR DEM using InSAR are discussed in [51,52]. The key step in DEM generation is the transformation of phase‐change measurements to elevations [53]. InSAR was developed by Graham [54]. In terms of economics, efficiency, and resolution, InSAR is the best DEM production technique [55]. InSAR calculates phase changes from two radar image pairs taken at different times and reveals changes in the surface quantitatively and qualitatively [56]. There are 6 steps in InSAR generation of DEMs from PALSAR data [52] (Figure 3), which are: (1) register radar images—in this study, two images (Slave (2008/07/11) and Master (2010/08/16)) were entered chronologically; (2) produce interferogram—the images iSRTMn SARScape4.8 are converted into
Remote Sens. 2020, 12, 874 5 of 24 Remote Sens. 2020, 12, x FOR PEER REVIEW 6 of 25
FigureFigure 2. Flowchart 2. Flowchart of of researchresearch in in the the study study area. area.
Table 1. Computation of basic, linear, shape, and relief morphometric parameters.
Parameters Parameters Formula References Area (A) - [15] Basin perimeter (P) - [15] Stream order - [17] Basic Basin length (Lb) - [15] Mean stream length (Lsm) LSM = Lu [17] Nu Stream length ratio (RL) RL = Lu [15] Lu+1 Bifurcation ratio (Rb) Rb = Nu [15] Nu+1 2 Lu Drainage density (Dd) (km/km ) Dd = A [15] 2 Nu Stream frequency (Fu) (no./km ) Fu = A [15] Mean bifurcation ratio (Rbm) - [17] Linear 2 Nu Texture Ratio (T) (no./km ) T = P [15] = 1 Length of Overland Flow (Lo) (km) Lo 2Dd [15] Infiltration number (If) I f = Fu Dd [17] ×A Constant of channel maintenance (C) C = Pi=1 [15] i=nLu Bh Ruggedness number (Rn) Rn = D [15] d × 1000 Relative relief (Bh) B = h h [15] Relief h − 1 Relief ratio (Rh) Rh = Bh [17] Lb Figure 3. Interferometric synthetic aperture radar (InSAR) data‐processingB procedure for digital Average slope (S) Sa = h 100 [17] elevation model (DEM) production. √A × A Form Factor (Rf) R f = 2 [15] Lb L2 Shape Factor (Bs) B = b [19] Shape s Aq A Elongation Ratio (Re) Re = 1.128 [15] Lb Cc = Compactness Coefficient (Cc) [15] 0.2821 P/A0.5 × A Circularity Ratio (Rc) Rc = 4 π [19] × × P2 2 Lu. Total stream length of all orders; Nu. Total number of stream segments of order “u”; A. Area of the basin (km ); P. Perimeter of the basin (km); Dd. Drainage density; Fu. Stream frequency; Bh. Basin relief; H. Maximum height; h1. 2 Minimum height; Lb. Basin length; and Lb . Square of the basin length. Remote Sens. 2020, 12, x FOR PEER REVIEW 6 of 25
Remote Sens. 2020, 12, 874 6 of 24
Morphometric (elevation and slope) and hydrologic (drainage networks) parameters were estimated using an ALOS PALSAR DEM (12.5 m resolution). Detailed descriptions of the methodology used to produce the ALOS PALSAR DEM using InSAR are discussed in [51,52]. The key step in DEM generation is the transformation of phase-change measurements to elevations [53]. InSAR was developed by Graham [54]. In terms of economics, efficiency, and resolution, InSAR is the best DEM production technique [55]. InSAR calculates phase changes from two radar image pairs taken at different times and reveals changes in the surface quantitatively and qualitatively [56]. There are 6 steps in InSAR generation of DEMs from PALSAR data [52] (Figure3), which are: (1) register radar images—in this study, two images (Slave (2008/07/11) and Master (2010/08/16)) were entered chronologically; (2) produce interferogram—the images iSRTMn SARScape4.8 are converted into single-look complex (SLC) format with the following settings, TB (745 days), SB (114.8 m), and critical baseline (2623.5 m); (3) remove flat effect—the DEM derived from the Shuttle Radar Topography Mission (SRTM) and its Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) files are used to remove the topographic effect; (4) filter noise—adaptive filters are used to eliminate the effect of noise from the interferogram, as they may reduce its quality (this step will also produce a coherent image, coherence indicates interferogram quality and should be less than 0.5; the Goldstein filter was used in this study and the coherence value of the image was 0.20); (5) unwrap phase—region-growing and minimum cost-flow algorithms are the most common methods for phase correction, a region-growing algorithm was used in this study; and (6) convert phase to height—the ALOS PALSAR DEM is created by phase-to-height conversion, theFigure PALSAR 2. Flowchart DEM of of research the study in the area study was area. produced with the mosaicking tools in ENVI v4.8.
FigureFigure 3. 3.Interferometric Interferometric synthetic synthetic aperture aperture radar radar (InSAR)(InSAR) data-processingdata‐processing procedure procedure for for digital digital elevation model (DEM) production.elevation model (DEM) production.
An ALOS PALSAR DEM enhanced reproduction of the complex topography of the study area to improve approximations of morphometric and hydrological factors [57,58]. The vertical accuracy of the ALOS DEM was assessed by ground-truthing the model’s elevation values in ArcGIS 10.5 at selected 230 ground control points (GCPs) (Figure4), following procedures used in [ 59]. The root mean square error (RMSE) of DEM generated based on comparisons with GCPs was 1.2 m. Remote Sens. 2020, 12, 874 7 of 24 Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 25
Figure 4. Ground control points in the study area. Figure 4. Ground control points in the study area.
The boundariesTable of the1. Computation sub-basins of basic, (Figure linear,5) shape, were and extracted relief morphometric by determining parameters. the pour point, the location where water drained from the basin flows into the main river. For drainage network extraction, Parameters Parameters Formula References the Arc Hydro package was used. Compared to the manual approach, this package provides a rational, Area (A) ‐ [15] effective, and consistent algorithmBasin [46 perimeter]. Generating (P) the drainage network‐ using Arc Hydro[15] has been Remoteexplained Sens. 2020 by Ahmad, 12, x FOR Rather PEER REVIEW et al. [Stream47 ]. To order do this, DEM sinks were specified‐ and filled to[17] designate 8 of the 25 flow direction and locations of accumulation.Basin length (Lb) Stream networks in the sub-basin‐ were defined[15] according Basic 𝐿 to the cumulative number ofMean upstream stream cells length draining (Lsm) into each cell.𝑅𝐿𝑆𝑀 A=4×𝜋× threshold = 𝐴 of greater [17] than 500 was Circularity Ratio (Rc) 𝑁 𝑃 [19] used to extract the drainage; this critical threshold is the minimum upstream𝐿 area necessary to produce Lu. Total stream length of allStream orders; length Nu. ratio Total (RL) number of stream𝑅𝐿 segments = of order “u”; [15]A. Area of 𝐿 +1 a stream. The areas2 and perimeters of the sub-basins were extracted by𝑁 computing the geometry of the the basin (km ); P. Perimeter Bifurcationof the basin ratio (km); (Rb) D d. Drainage density;𝑅𝑏 = Fu. Stream frequency;[15] B h. Basin 𝑁 +1 sub-basin polygons. Strahler’s1 scheme was used forb stream ordering [162 ]. relief; H. Maximum height; h . Minimum height; L . Basin length; and Lb𝐿. Square of the basin length. Drainage density (Dd) (km/km2) 𝐷 = 𝐴 [15] 𝑁 Stream frequency (Fu) (no./km2) 𝐹 = 𝐴 [15] Mean bifurcation ratio (Rbm) ‐ [17] 𝑁 Linear Texture Ratio (T) (no./km2) 𝑇= 𝑃 [15] 1 Length of Overland Flow (Lo) (km) 𝐿 = 2𝐷𝑑 [15] Infiltration number (If) 𝐼𝑓 =𝐹 ×𝐷 [17] 𝐴 Constant of channel maintenance (C) 𝐶= [15] ∑ 𝐿 𝐵 Ruggedness number (Rn) 𝑅 =𝐷 × 1000 [15] Relative relief (Bh) 𝐵 =ℎ−ℎ [15] 𝐵 Relief Relief ratio (Rh) 𝑅ℎ = [17] 𝐿 𝐵 Average slope (S) 𝑆𝑎 = × 100 [17] √𝐴 𝐴 Form Factor (Rf) 𝑅 = [15] 𝐿 𝐿 Shape Factor (Bs) 𝐵 = [19] Shape 𝐴 𝐴 Elongation Ratio (Re) 𝑅 = 1.128 [15] 𝐿 Compactness Coefficient (Cc) 𝐶𝑐 = 0.2821 ×𝑃⁄𝐴 . [15]
Figure 5. Sub-basins of the Kalvar¯ ¯ı basin. Figure 5. Sub‐basins of the Kalvārī basin.
2.3. Models
2.3.1. COPRAS COPRAS is an MCDM method presented by Zavadskas and Kaklauskas in 1996 [60]. The COPRAS method assumes direct and commensurate affiliations of the levels of magnitude and usefulness of alternatives in the presence of conflicting criteria [61]. The COPRAS procedure consists of the following steps [62]: Step 1: Prepare the primary matrix; Step 2: Normalize the primary matrix using Equation (1):
𝑥 𝑥 = (1) ∑ 𝑥
th th where 𝑥 is the normalized quantity of the j criterion, 𝑥 is the i alternative performance of the jth criterion, and m denotes the alternative numbers; Step 3: Determine the normalized weighted decision‐making matrix (Equation (2)):
𝑑 =𝑤 ×𝑥 (2)
th where 𝑥 is the efficiency of the i alternative, and wj is the criterion weight; Step 4: Compute the maximum and minimum indices for alternatives – in this step, alternatives are classified as maximising and minimising indices (Equations. (3) and (4)):