## 4 Discussion  
In this study, the most suitable model and algorithm for predicting future wind speed were identified by applying a series of machine learning algorithms alongside SARIMA, a stochastic method, using monthly measured wind speeds in the Bodo region. The findings of this study align with results from previous research in the literature. For instance, [PERSON] [84] modeled wind energy data from a wind turbine located in the Senkoy region using LSTM, ANN, the recurrent neural network (RNN), and the convolutional neural network (CNN). Instead of directly modeling wind speed, his study focused on predicting the electrical energy generated from wind speed, utilizing daily data from 2018. The models were evaluated using performance metrics such as root mean square error (RMSE), the coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), and mean squared error (MSE). The study concluded that LSTM outperformed other methods in terms of predictive accuracy. Similarly, [PERSON] and [PERSON] [85] analyzed wind speed data from a wind turbine in Canakkale Gelibolu using LSTM and ARIMA, a stochastic method, to develop future forecasting models. Their study was based on daily data collected between 2014 and 2021. Unlike this study, they did not create a specific model structure. However, their findings indicated that LSTM outperformed ARIMA in predictive performance, aligning with the results of this study. Machine learning algorithms are widely used in the literature for estimating meteorological and hydrological parameters. One example is the estimation of drought indices. [PERSON] et al. [86] developed future-oriented drought prediction models for six different regions in Norway using four different algorithms, including LSTM. They further enhanced  
Figure 11: The results of the best models in Ridge chart where _LSTM-WM02_ analysis of LSTM with WT for M04, _SVM-WM05_ analysis of SVM with WT for M05, etc. their models using WT. Notably, the Bodo region, which is part of the present study, was also included in their analysis. Their findings indicated that LSTM was among the most effective algorithms for drought analysis, even without WT. The methods and results of their study closely align with the present study. Another study on wind speed prediction was conducted by [PERSON] et al. [87], who employed both SARIMA and LSTM models without a predefined model structure. Their research in the El-Oued region found that LSTM outperformed SARIMA in predictive accuracy. Overall, the findings of this study are consistent with those of the aforementioned studies, demonstrating that SARIMA generally yields lower predictive performance compared to machine learning methods, including models enhanced with WT. Similar performance trend is not limited to wind speed but extends to other meteorological and hydrological parameters as well. For example, [PERSON] et al. [88] examined drought prediction using rainfall data recorded between 1926 and 2020 from the central meteorological station in Konya. Their approach was first calculating the drought index and then deriving the 3-month and 12-month values of the standardized precipitation index (SPI) values. Using these data, they developed five different models based on cross-correlation and analyzed them using various modifications of LSTM and SVM. While their results indicated that LSTM underperformed compared to SVM, they emphasized that LSTM still exhibited strong performance metrics. In contrast, the present study found the opposite trend, with LSTM outperforming SVM in wind speed prediction. One of the most notable findings of this study is the relatively poor performance of ANN combined with WT. While numerous studies in the literature [89; 90; 91] highlight the effectiveness of ANN with WT in predicting meteorological parameters, this combination did not yield good results for wind speed prediction in this region. Therefore, ANN with WT may not be a suitable approach for wind speed forecasting in this specific location.