Mohammadjavad Vahidi; Rasoul Mirabbasi Najafabadi; Mohsen Ahmadi
Abstract
1- Introduction Soil erosion has significant environmental impacts and economic losses on crops and reservoir capacity, and affects water quality both directly and indirectly (Issaka and Ashraf, 2017: 3). Therefore, identifying factors affecting soil erosion and ranking the prevention methods in rural ...
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1- Introduction Soil erosion has significant environmental impacts and economic losses on crops and reservoir capacity, and affects water quality both directly and indirectly (Issaka and Ashraf, 2017: 3). Therefore, identifying factors affecting soil erosion and ranking the prevention methods in rural areas provide valuable information for managers and planners for soil conservation (Asadi et al., 2016: 99). Soil erosion, on the one hand, is affected by natural features and, on the other hand, by human activities. The type of precipitation regime and water erosion, erosion-sensitive formations, low natural vegetation cover, topographic conditions of natural factors, incorrect use or overuse of lands, poor pasture grazing, plowing of low-yield rainfed fields, and implementation of construction projects, such as road construction, building construction, and mining, without considering the principles of soil protection are the factors caused by human intervention in the country (Darabi et al., 2018: 201). The most appropriate scientific methods can be selected to prevent soil erosion in accordance with the opinion of relevant experts and scientists. Another important issue is the use of appropriate criteria and sub-criteria to obtain final responses. In fact, if the study method is selected properly, but the criteria and sub-criteria used are not of the desired quality, the results are not reliable and the final output will cause deviations in the final decision. Therefore, it is necessary to extract all required criteria and sub-criteria from the research literature and validate them by experts. Among the methods used to control soil erosion are contour plowing, mulching, mixed cultivation, adding organic fertilizers and manure, grass cultivation, terracing, and cultivation on contour lines (Begum Nasir Ahmad et al., 2020: 104). Darmian County is one of the important agricultural centers of South Khorasan province. As reported previously, 93.3% of the total area is in the severe desertification class (Parvaneh, 2009: 150). According to most studies on multi-criteria decision making techniques (MCDM), the VIKOR technique results in a lower percentage and intensity of changes and yields more valid results (Nazmfar and Padarvandy, 2015: 36; Kim and Ahn, 2019: 126). Therefore, this technique was used in the present study. Keshtkar et al. (2017: 133) conducted a study with the aim of prioritizing the biological management options of Delichay watershed using MCDM. They identified four biological management activities and developed 16 management scenarios in the region. Also, the social, ecological, economic, and physical criteria were assigned the first to fourth priorities, respectively, and scenario number 10 (grazing management and pit-seeding) was determined as the top scenario in the first priority. Vulević et al. (2015: 317) prioritized soil erosion vulnerable areas in the Topčiderska River Watershed, northern Serbia, using multi-criteria analysis methods, and identified the most vulnerable sub-basins due to a significant presence of arable and very steep arable lands, which, therefore, had priority for protection. Also, Zhang et al. (2020: 1331) identified priority areas for soil and water conservation planning using multi-criteria decision analysis in the Xinshui River watershed, China. They selected six assessment indicators, including slope gradient, precipitation, NDVI, land use, soil texture, and slope aspect. They concluded that more attention should be paid to the slope of farmland and grassland during the planning and management of soil and water conservation projects. Darmian County is an important region in terms of agricultural and horticultural products and severe erosion that threatens the products and natural resources in rural areas. In this research, therefore, an integrated approach based on the multi-criteria decision making methods, including Best-Worst (BWM) and the VIKOR methods, is presented according to expert’s opinions to analyze the factors affecting soil erosion and ranking the prevention methods in the rural region of Darmian, South Khorasan Province. 2- Methodology In this study, the weights of the identified criteria and sub-criteria from the research literature and experts’ opinion were first determined using the BWM and then the VIKOR method was used for ranking the erosion prevention methods. According to the review of the literature, many methods have been proposed to rank the methods of preventing soil erosion. However, these methods usually have a relative level of uncertainty due to a high level of decision maker involvement in the production of final answers. However, the best-worst method has a very strong approach in determining the weight of criteria compared to other decision-making methods (Rezaei, 2016: 126). Best-worst method: This is one of the powerful methods in solving the multi-criteria decision making problems used to obtain the weights of options and criteria (Rezaei, 2016:126). This method compensates for the weaknesses of methods based on pairwise comparisons (e.g., AHP and ANP) such as incompatibility. In addition, it reduces the number of pairwise comparisons significantly by only performing reference comparisons. In recent years, the best-worst method has been used by many researchers to determine the weights and rankings of options in various fields. VIKOR method: This method is an adaptive ranking technique that is often used in situations with different conflicting criteria (Opricovic, 1998: 5). This method creates a compromise solution based on "proximity to the ideal solution and mutual agreement through concessions". This method has been widely used by many researchers to rank options (Arab Ameri et al., 2018: 1400; Gupta, 2018: 47; Opricovic and Tzeng, 2004: 445). It uses linear normalization that specifies a summation function indicating the distance from the ideal solution. 3- Results and Discussion The criteria and sub-criteria used in this research (based on a review of the research literature) are presented in Table 1. Table (1): The criteria and sub-criteria affecting soil erosion Sub-criteria Criteria Sub-criteria Criteria Aggregate stability Technical Destruction of vegetation Environmental Water penetration capacity Surface water flows Depth of soil Runoff volume Clay particles Chemical Destruction of ecosystems Soil organic carbon content Drought Climatic Non-use of livestock manures Social Fire Rainfall Overgrazing Land slope A consensus method was used to achieve valid results, as for gathering information, a committee of experts was asked to evaluate the performance of the options against the criteria (Table 1) using the scales listed in Table 2. Table (2): Verbal scale for pairwise comparisons of best-worst methods and Victor techniques Scale for the best- worst approach EquallyImp. Equal to moderately Imp. Moderately Imp. Moderately to strongly Imp. Strongly Imp. Strongly to very strongly Imp. Very strongly Imp. Very strongly to extremely Imp. Extremely Imp. 1 2 3 4 5 6 7 8 9 Scale for Victor technique Verbal expressions Degree of Imp. for negative effect criterion Degree of Imp. for positive effect criterion Least Imp. 5 1 Moderately Imp. 4 2 Strongly Imp. 3 3 Very Strongly Imp. 2 4 Extremely Imp. 1 5 P.S. Imp. = Important Calculating the weights of the criteria using the best-worst method Out of all the criteria, the best and worst criteria were selected by experts through mutual agreement. The priority of other criteria was also determined by the worst criteria. After collecting the best-worst method questionnaires, the weights related to the criteria and sub-criteria were obtained using the GAMS optimization software version 24.3 by the BARON solver. The degrees of priority for all the criteria were achieved to calculate the optimized local weights. The results showed that the "technical" and the "chemical" criteria had the highest (0.293) and the lowest (0.085) local weights, respectively, among all the examined criteria. Prioritization of erosion prevention methods using the VIKOR method After achieving the weights of the criteria, the methods of erosion prevention were prioritized in the next step based on the weights of these factors using the VIKOR method. According to the computational results, the technical and the chemical criteria (with scores of 0.293 and 0.085) had the highest and the lowest ranks, respectively. In the final prioritization of the erosion prevention methods, Biochar and injection of organic maters were in the first and second ranks, respectively, and artificial rain was at the lowest rank. 4- Conclusion In this research, a new combined approach is presented based on the best-worst method and VIKOR technique to identify the factors affecting soil erosion and to rank the prevention methods based on the opinions of experts and scientists in the field of agricultural development. According to the obtained results, "technical", "climatic", and "environmental" sub-criteria are the three important factors in evaluating erosion prevention methods. In the next step, the options were finally ranked using the VIKOR method, indicating that the top three options are "Biochar", "Arch planting", and "injection of fertilizers and organic matter", respectively. Considering that the development of infrastructure to select scientific methods to prevent soil erosion in rural areas is one of the effective factors in the development of agricultural science in the country, studies in this area should be given more attention. It is expected that the results of this research can provide a suitable tool for managers to make correct decisions.
Mohammadtaghi Sattari; Rasoul Mirabbai Najafabadi; Masood Alimohammadi
Volume 3, Issue 8 , December 2016, , Pages 73-92
Abstract
Received: 2015.08.16 Accepted: 2016.11.18 Mohammadtaghi Sattari[1]* Rasoul Mirabbasi Najafabadi[2] Masood Alimohammadi[3] Abstract Accurate prediction of droughts in arid and semi-arid countries, like Iran, have important role in water resources management and designing appropriate plans for coping with ...
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Received: 2015.08.16 Accepted: 2016.11.18 Mohammadtaghi Sattari[1]* Rasoul Mirabbasi Najafabadi[2] Masood Alimohammadi[3] Abstract Accurate prediction of droughts in arid and semi-arid countries, like Iran, have important role in water resources management and designing appropriate plans for coping with drought consequences. Since the standardized precipitation index (SPI) is known as a suitable index for drought analysis, in this study, we used the M5 rule tree model for forecasting SPI values. For this purpose, the monthly precipitation data of Maragheh synoptic station were used during a 25-year period for calculating SPI values at 6-month time scale (SPI-6). The results indicated that the Maragheh region was faced with successive and severe droughts in recent two decays. In the next step, the SPI-6 values were forecasted for next 1 to 12 months using M5 rule tree model. The results showed that the SPI-6 values in previous time steps had the most effect on forecasting the next SPI-6 values, and the forecasting accuracy decreases with increasing prediction length. So the correlation coefficient of forecasting SPI-6 for next month was obtained 0.94 which this value was decreased to about 0.40 for forecasting SPI-6 for next 12 months. However, the M5 rule tree model provides more understandable, applicable and simple linear relation in forecasting droughts and shows relatively good performance and accuracy. [1]- Assistant Professor, Department of Water Engineering, University of Tabriz (Corresponding Autor), Email:mail:mtsattar@gmail.com. [2]- Assistant Professor, Department of Water Engineering, Shahrekord University. [3]- MSc of Civil Engineering.
