A New Risk-Based Method in Decision Making to Create Dust Sources Maps: A Case Study of Saudi Arabia.

Dust storms are one of the major causes of the destruction of natural ecosystems and human infrastructure worldwide. Therefore, the identification and mapping of susceptible regions to dust storm formation (SRDSFs) is of great importance. Determining SRDSFs by considering the concept of risk in the decision-making process and the kind of manager’s attitude and planning can be very valuable in dedicating financial resources and time to identifying and controlling the negative impacts of SRDSFs. The purpose of this study was to present a new risk-based method in decision making to create SRDSF maps of pessimistic and optimistic scenarios. To achieve the purpose of this research, effective criteria obtained from various sources were used, including simulated surface data, satellite products, and soil data of Saudi Arabia. These effective criteria included vegetation cover, soil moisture, soil erodibility, wind speed, precipitation, and absolute air humidity. For this purpose, the ordered weighted averaging (OWA) model was employed to generate existing SRDSF maps in different scenarios. The results showed that the wind speed and precipitation criteria had the highest and lowest impact in identifying dust centers, respectively. The areas identified as SRDSFs in very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 85,950, 168,275, 255,225, 410,000, and 596,500 km2, respectively. The overall accuracy of very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 84.1, 83.3, 81.6, 78.2, and 73.2%, respectively. The very pessimistic scenario can identify the SRDSFs in the study area with higher accuracy. The overall accuracy of the results of these scenarios compared to the dust sources obtained from the previous studies were 92.7, 94.2, 95.1, 88.4, and 79.7% respectively. The dust sources identified in the previous studies have a higher agreement with the results of the neutral scenario. The proposed method has high flexibility in producing a wide range of SRDSF maps in very pessimistic to very optimistic scenarios. The results of the pessimistic scenarios are suitable for risk-averse managers with limited financial resources and time, and the results of the optimistic scenarios are suitable for risk-taking managers with sufficient financial resources and time.