## 4 Conclusions  
This study modeled future droughts in the Tromso region using a combination of ANFIS with various optimization algorithms and PSO-optimized SVM, based on the monthly precipitation data. Different model input structures employed for incorporating SPI12 and SPI3 data. Additionally, 70% of the data was used for training, and all findings  
Figure 12: RSR graphs for some models with the best performance values, where ANFIS-ABC analysis of ANFIS with ABC optimization, ANFIS-GA analysis of ANFIS with GA optimization, SVM-PSO analysis of SVM with PSO optimization, etc.  
presented in this study are based on the remaining 30% test data. The key findings are summarized below:  
* ANFIS-ABC-M04 emerged as the most successful model in this study. This model and its corresponding input structures are recommended for future drought prediction modeling studies in this region. Given its strong performance, after ANFIS-ABC-M04 model, SVM-PSO-M06 should be another model that can be preferred as a viable alternative.
* Models using SPI12 data in their input structure consistently outperformed those incorporating SPI3 data.
* The input structures of M01, M02, and M03 were created using SPI12 data lagged up to 2, 3, and 4 months, respectively. These models demonstrated a lower performance compared to the M04, M05, and M06 models. As a result, incorporating SPI12 lagged by 11, 12, and 13 months, in addition to those shorter lags, positively impacted the model performance, similar to SPI3 data.
* All algorithms, except for ANFIS-GWO, produced comparable results. The ANFIS-GWO model yielded significantly different outcomes, indicating potential limitations of this algorithm under the specific conditions of the selected region.
* Among the models and algorithms using SPI3 data in their input structure, the ANFIS-GA algorithm with the M09 model/input structure was determined as the most effective.
* Drought analyses displayed higher extremely dry values for SPI3 compared to SPI12.
* The ridge chart visualization approach yielded results that did not match the statistical findings for this region.
* Graphical evaluation yielded the most effective results, with Taylor and Violin diagrams. These diagrams are recommended for use in future drought modeling studies based on the superior insights they provided compared to the others.
* The basic parameters in creating the Taylor diagram are the RMSE, standard deviation, and correlation coefficient. Accordingly, most of the patterns detected in this diagram overlap. NSE was used in the statistical evaluation in this study. This shows that it is an effective parameter used to distinguish performance between models.
* The utilization of the ABC optimization approach in drought prediction models is recommended due to its superior effectiveness compared to alternative optimization methods. It should be paired with different ML and DL algorithms and employed in future studies.
* Scatter diagrams should be used to evaluate the model performance in prediction models, as they provide precise information into whether the peak values can be predicted accurately or not.  
Conceptualization was done by [PERSON] and [PERSON]; methodology was done by [PERSON], [PERSON] and [PERSON]; writing--review and editing was done by [PERSON], [PERSON] and [PERSON]; visualization was done by [PERSON], [PERSON] and [PERSON]; and supervision was done by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. APC was supported by UiT, the Arctic University of Norway.  
Not applicable.  
The original contributions presented in the study are included in the article. The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request.  
The authors declare that there are no conflicts of interest.