## 2 Dataset and Methodology
### Site Selection Results
The aspect (3.13%) was also considered, with south-facing slopes (S2) receiving the highest value, as they maximize solar energy capture. Lastly, wind speed (2.46%) was included, with moderate wind speeds (0-3 m/s) being rated as highly suitable to prevent structural concerns while maintaining efficiency. Overall, these criteria collectively define the most suitable regions for PV farm location, ensuring optimal energy production and infrastructure feasibility. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Criteria** & **GHI** & **LULC** & \begin{tabular}{c} **Distance** \\ **to Road** \\ \end{tabular} & \begin{tabular}{c} **Distance** \\ **to Fault** \\ \end{tabular} & \begin{tabular}{c} **Distance to** \\ **Powerline** \\ \end{tabular} & **Slope** & **Aspect** & \begin{tabular}{c} **Wind** \\ **Speed** \\ \end{tabular} \\ \hline \begin{tabular}{c} GHI \\ \end{tabular} & 1.0 & 5.0 & 3.0 & 5.0 & 5.0 & 7.0 & 9.0 & 7.0 \\ \hline \begin{tabular}{c} LULC \\ \end{tabular} & 0.2 & 1.0 & 3.0 & 3.0 & 3.0 & 5.0 & 7.0 & 5.0 \\ \hline \begin{tabular}{c} Distance to \\ Road \\ \end{tabular} & 0.3 & 0.3 & 1.0 & 3.0 & 3.0 & 3.0 & 5.0 & 5.0 \\ \hline \begin{tabular}{c} Distance to \\ Fault \\ \end{tabular} & 0.2 & 0.3 & 0.3 & 1.0 & 3.0 & 3.0 & 3.0 & 5.0 \\ \hline \begin{tabular}{c} Distance to \\ Powerline \\ \end{tabular} & 0.2 & 0.3 & 0.3 & 0.3 & 1.0 & 3.0 & 3.0 & 3.0 \\ \hline \begin{tabular}{c} Slope \\ \end{tabular} & 0.1 & 0.2 & 0.3 & 0.3 & 0.3 & 1.0 & 3.0 \\ \hline \
\begin{tabular}{c} Aspect \\ \end{tabular} & 0.1 & 0.1 & 0.2 & 0.3 & 0.3 & 0.3 & 1.0 & 3.0 \\ \hline
\begin{tabular}{c} Wind Speed \\ \end{tabular} & 0.1 & 0.2 & 0.2 & 0.2 & 0.3 & 0.3 & 0.3 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pairwise comparison matrix. In MCDM processes, suitability maps differ due to the varying weights assigned to both sub-criteria and main criteria. The suitability maps, generated through GIS-based operations, can be expressed using different classification methods, such as manual, equal interval, defined interval, quantile, natural breaks, geometric interval, and standard deviation [26, 29, 59]. In this study, considering the weighting of criteria, the suitability ranges were determined using the natural breaks method. This method allows for the optimal classification of data by minimizing the variance within classes and maximizing the variance between them, leading to more meaningful and accurate suitability maps for the analysis. Additionally, when comparing the natural breaks method with the quantile method, there was some variation between the unsuitable and low suitability classes. Natural breaks are particularly effective when the data distribution has distinct clusters or natural groupings, which makes it an ideal choice for this study. Dataset maps prepared according to these distributions are shown in Figure 3.  
When determining the class intervals for datasets, the class ranges from similar studies in the literature were examined to select appropriate values. These intervals were chosen to align with the characteristics of the study area and the datasets. Consequently, the data were reclassified into five categories and integrated into the analysis process, ensuring consistency and comparability across the criteria (Figure 3). To assign weights to the criterion maps, these map layers must be standardized on the same scale based on their threshold values. Table 5 presents the classification intervals used for reclassifying the data. This was achieved by reclassifying the criterion layers on a scale of 1-5, where 1 represents unsuitable areas, and 2-5 indicate increasing levels of suitability, ranging from low (2) to high (5). The reclassification ensures a consistent evaluation framework, enabling effective integration and comparison of various criteria within the AHP process.