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        1 - Integration of decision-making models based on optimization, distance ratio and additive weighting in climate pattern determination
        Laleh Parviz Neda Azizi Khadijeh Khani-Zangbar
        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, te More
        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. Manuscript profile
      • Open Access Article

        2 - Improving hybrid modeling using an efficient model for rainfall forecasting
        Laleh Parviz
        Low-precision rainfall forecasting leads to significant losses in various sectors such as agriculture and the environment. In this regard, the effect of support vector regression (SVR), gene expression programming (GEP) and group data modeling (GMDH) models on improving More
        Low-precision rainfall forecasting leads to significant losses in various sectors such as agriculture and the environment. In this regard, the effect of support vector regression (SVR), gene expression programming (GEP) and group data modeling (GMDH) models on improving the performance of the hybrid model was examined, which is based on station rainfall data. Urmia and Isfahan with two different climates were used in the period 1964-2019. In nonlinear section modeling, the third combination with the linear section combination, residuals and observational data in the previous time step had less error, for example in Isfahan station, the rate of RMSE reduction from combination 1 to 3.73 / 62 And the rate of SMAPE reduction from 2 to 3 was equal to 62.79%. The hybrid model had better performance than the stochastic model, so that the amount of RMSE from the stochastic model to the hybrid model with SVR, GEP and GMDH at Urmia station decreased by 79.46, 68.34 and 75.77%, respectively. . The gene expression programming model was less accurate than the other models studied (in Urmia station, the rate of UII reduction from GEP to SVR model was 32.5 and 15.62%, respectively, and in Isfahan station, the rate of increase in Nash coefficient was Sutcliffe from GEP to GMDH was 22.38). The amount of Nash-Sutcliffe coefficient in all three models in Urmia station was higher than Isfahan (the average rate of decrease in Nash-Sutcliffe coefficient from Urmia station to Isfahan was 6.22%) but the value of coefficient in both stations is within acceptable range. Therefore, choosing an efficient model with the right combination in nonlinear modeling will have a significant effect on increasing the efficiency of the hybrid model. Manuscript profile