Integration of decision-making models based on optimization, distance ratio and additive weighting in climate pattern determination

Subject Areas : Climatology

Laleh  Parviz ¹ (Associate Professor, Faculty of Agriculture, Shahid Madani University of Azerbaijan, Tabriz, Iran)
Neda  Azizi ² (Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran)
Khadijeh  Khani-Zangbar ³ (Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran)

Received: 2023-09-09 Accepted : 2024-01-27 Published : 2023-11-22

Keywords: Ranking, Index, Meteorological data, Extreme,

Abstract :

Climate indices by revealing the climatic diversity of the region, have led to the development of management policies in agriculture, water resources and environment fields. The performance of De Martonne, Ivanov, precipitation effectiveness, continental coefficient, temperature, rainfall anomaly, percent of normal precipitation, vegetation, aridity and Selyaninov indices were investigated using the data of 15 meteorological stations. The effective climate index determination was done using simple additive weighting (SAW), TOPSIS and simultaneous evaluation of criteria and alternatives (SECA). The sensitivity analysis of the SECA method rather to the β coefficient had a significant effect on the results. Based on the ranking results of three multi-criteria decision-making methods, Ivanov's index performs well in severe climate conditions (with extreme high and low values), and in other climatic conditions, it is better to use it together with another climate index. The percent of normal precipitation index was overestimated in most of the stations. Rainfall anomaly index also described the climatic condition of most stations as close to normal. In determining the effective climate index, the number of meteorological data, the type of their mathematical relationship and the way of climatic demarcation are of special importance. The highest amount of intensity and percentage of changes was in the case of SAW and SECA, TOPSIS and SECA methods. The highest number of first ranks in three multi-criteria decision-making methods is related to De Martonne, aridity, vegetation indices and then effective precipitation index.