Assessing Desertification Risk Potential Using Fuzzy Taxonomy Adaptive Model in GIS Environment in Yazd-Khazarabad Sub-basin

Background and Purpose: Desertification is one of the most serious ecological crises, and its control is considered a national concern. Given the increasing spread of this phenomenon and the emergence of its extensive and long-term effects on the environment and human activities, by providing appropriate management methods within the framework of desert management, the intensity and spread of this phenomenon can be reduced and the high costs of incorrect decision-making can be prevented. Therefore, implementation measures in this field should be based on understanding the current state of desertification and its severity. Therefore, the present study aimed to evaluate and zone the desertification event using multi-attribute decision-making models and geographic information system techniques as a case study in the Yazd-Khazarabad sub-basin during the years 2023 to 2024.
Methodology: In this research, an attempt was made to accomplish this using the fuzzy taxonomy method. Therefore, after determining the members of the decision-making team consisting of experts familiar with the study area, effective indices were determined and evaluated using the fuzzy Delphi method. In order to select these indicators, three main axes of relationship with the desertification phenomenon, ease of access, and ease of updating were considered within the framework of two factors: cost and time. Then, in order to prepare a suitable framework for the zoning map of the desertification process vulnerability, work units were separated using the geomorphological method in the ArcGIS software environment. the data were fuzzy by the Chen and Huang methods. The fuzzy analysis process was performed on the data according to the following steps:
- Determining fuzzy values for the weights of indicators, units of work and forming a matrix of fuzzy numbers
- Calculate the value of ( D) ̃_K or Synthetic Trapezoidal Fuzzy Number for each row of fuzzy matrix from
After estimating the combined trapezoidal fuzzy numbers, de-fuzzy action was performed and the decision matrix was formed through the following steps.
- Calculate the value of D ̃_K of each row of fuzzy matrix relative to each other in the MATLAB software environment
- Calculate the value of each combined trapezoidal fuzzy numbers from K another hybrid fuzzy number
- Normalization of abnormal weights of indices and work units
- Forming the fuzzy decision matrix
Then, in the framework of the fuzzy decision matrix and the TAXONOMY method, the intensity of desertification was estimated through the following step:
- Formation of the Harmonic Fuzzy Decision Matrix (HFDM) is obtained
- Formation of the desertification intensity matrix of indices and from work units
- Estimation of taxonomic distance of work units
- Calculating the maximum possible taxonomic
- Estimating the taxonomic rank (yi) in work units from the relationship and ranking the work units based on the ranks obtained.
Finally, in order to facilitate and accurately analyze data and achieve results, a mapping of desertification potential was carried out based on the taxonomic degree of the working units and using Arc view 3.2a software.
Findings and Discussion: After determining the balanced fuzzy decision matrix, according to the research literature, taxonomic distance (gi) and taxonomic degree (yi) or in other words, desertification intensity was obtained by separating the working units. Finally, in order to facilitate reading and understanding of the estimated results and to show regional differences in vulnerability to desertification, a final map of desertification intensity potential was formed based on the desertification intensity (yi) values of the working units. The results showed that 35.72 and 17.28 percent of the study area located in very high and high desertification classes, respectively. Moderately severe desertification (36.32 percent) has the largest share in the study area. In general, the quantitative value of desertification intensity for the entire area was evaluated as 0.74 in high or IV class from the total factors.
Conclusion: The study showed the efficiency and ease of application of fuzzy logic in the form of a taxonomy model in assessing the intensity of desertification. The results of this study provide the possibility of planning to minimize desertification as a result of development projects, and can create conditions where a balance between development plans and the environment is possible based on the priorities and vulnerability zoning of the study area.