## 3 Results and Discussion  
In this study, the SPI12 and SPI3 indices, which better represent meteorological and hydrological drought, respectively, were derived using monthly precipitation data from the Tromso region [98]. The categorization of drought levels based on SPI values was detailed earlier (Table 2). The calculations revealed, as depicted in Figure 5, that both the 12-month and 3-month SPI captured dry and wet periods across all drought classes. According to Figure 5, the most severe drought condition recorded for the SPI12 index occurred in September 1950 (9.1950), while November 1952 (11.1952) marked the most extreme condition for the SPI3 index. Additionally, further extremely dry periods were observed in September 1935; February, March, April, and June 1950; April 1960; and February 1979, as recorded in the SPI12 index, while the SPI3 index identified significant droughs in September 1957, August 1969, August 1978, June 1987, October 1995, October 1996, August 2015, November 2022, and July 2022. Some of the dry periods are marked as red circles in Figure 5. It was determined that the transitions between dry periods and wet periods were higher in SPI3 due to higher variance values compared to SPI12. Documented in Table 3, the following model input structures were created based on the data derived from SPI12 and SPI3. The models, constructed with the specified input structure, were evaluated using the optimization techniques outlined earlier, including ANFIS-PSO, ANFIS-GA, ANFIS-GWO, and ANFIS-ABC, as well as SVM-PSO. Consistent with the findings of the existing literature, 70% of the data was employed as learning in the analyses, whereas the remaining 30% was for testing [39; 98]. Detailed results from the analysis, applied methodologies for testing data, can be found in Table 4.  
Based on the detailed results in Table 4, where the most successful results are highlighted in bold, M04 operating under the ANFIS-ABC algorithm, achieved r: 0.9516, NSE: 0.9054, and RMSE: 0.3108 and emerged as the best performer among all the algorithms tested. The input for this model was structured using SPI values lagged by 1 and 2 months, as well as by 11, 12, and 13 months. The M06 model with r: 0.9515, NSE: 0.9050, and RMSE: 0.3114 and M05 model with r: 0.9512, NSE: 0.9042, and RMSE: 0.3127 within the  
Figure 5: SPI12 and SPI3 values calculated. ANFIS-ABC category also yield a very similar performance to those achieved by this M04 model. Additionally, in this category, among the models containing SPI3 data in the input structure, the most appropriate result was M12, which is an indication that it is the most suitable model structure. The model, in this category, demonstrated superior performance and predictive capabilities, compared to other model data, including SPI3, combinations evaluated in the study. The results underscore the synergistic relationship between the M12 model's architecture and the informative content embedded within the SPI3 dataset. This specific combination appears to have effectively captured underlying patterns and trends in the data, leading to enhanced predictive accuracy and overall model efficacy. The convergence in the performance parameters, with slight variations among these models, indicate that they are both leading models such as M04 and suggest that each setup has demonstrated an ability to provide representation of the underlying climate dynamics. The slight underperformance of models M06 and M05 compared to M04 can be attributed to the use of SPI values lagged by 2 and 1 months of those models, respectively. Comparatively, the models utilizing SPI12 data (M01-M06) demonstrated an overall better performance than those configured with SPI3 inputs (M07-M12). This distinction underlines the importance of selecting appropriate SPI indices and their temporal lagged values to optimize model efficiency. Applying SVM-PSO to M06 generated another successful outcome from the analysis following the M04 model in ANFIS-ABC.