## 4 Results  
Hyperparameters used for training the respective algorithm are presented in Table 2, and were obtained through trial and error. The most influencing hyperparameters that affected the performance of SVM were kernel type, cost parameter, and gamma value. The SVM reported the best performance using a polynomial kernel with degree one, cost value of 0.02, and gamma value of 0.16. SVM shows the best result at a gamma value of 1/6, with the polynomial kernel of degree 1 and the cost parameter having a value of 0.02; consequently, an accuracy greater than 95% was achieved for both training and evaluation datasets (Table 3). For ANN, we used an Adam optimizer with a learning rate of 0.0001; the SGD optimizer was nearly three times more computationally expensive during the training compared to the Adam optimizer. The same training samples, collected after preprocessing the data, were fed to both algorithms. Both algorithms reported good results, with training and validation accuracy of more than 90%. Table 3 compares the training accuracy, validation accuracy, and kappa coefficient of SVM with ANN. Both algorithms agree with the training dataset by having a kappa coefficient value of 0.95. SVM offers the training and testing accuracy of 95.98% and 95.61%, similar to respective accuracies of 94.48% and 95.73% for ANN. Tables 4 and 5 report the PA and UA of ANN and SVM for all the lithological units. The UA for all lithologies was greater than 90% for the ANN model except for the Mami Khel and Kamlial formations, with 88.1% and 88.6% accuracies. The highest UA shown by the ANN model was 99.8% for water and 99.5% for Jatta Gypsum. The PA of ANN was greater than 90% for all lithologies except for the Nagri and Muree formations, with 88.7% and 77.4% accuracies. ANN showed the highest PA, 99.5% for Jatta Gypsum, and 99.1% for the Kohat Formation. The SVM UA for all lithologies was greater than 90% except for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lithological Units** & **Time Scale** & **Training Samples** & **Testing Samples** \\ \hline Allnuum (A) & Miocene & 2561 & 1100 \\ Dhok Patan (D) & Miocene & 1430 & 625 \\ Nagri (N) & Miocene & 1701 & 725 \\ Chiniji(C) & Miocene & 1408 & 589 \\ Kamlial Sandstone (KS) & Miocene & 1993 & 833 \\ Murree (M) & Miocene & 548 & 261 \\ Kohat (K) & Eocene & 3042 & 1297 \\ Mami Khel Clay (MK) & Eocene & 564 & 249 \\ Jatta Gypsum (G) & Eocene & 2745 & 1100 \\ Water (W) & NA & 1073 & 496 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The training and testing samples for different classes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**SVM** & **ANN** \\ \hline Kernel type & 1 st-degree polynomial & Number of hidden layers & 4 \\ \hline Gamma (g) & 1/6 & Activation function & ReLU and softmax \\ \hline Cost (C) & 0.02 & Loss function & Categorical cross-entropy \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Important hyperparameters of SVM and ANN used in the study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Training Accuracy** & **Testing Accuracy** & **Kappa Coefficient** \\ \hline SVM & 95.98 & 95.61 & 0.95 \\ \hline ANN & 94.48 & 95.73 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training, validation accuracies, and kappa coefficient of SVM and ANN. the Murree Formation, with a UA of 84.9%. The highest UA reported by SVM was for water, i.e., 100% and 99.6% for Jatta Gypsum. The PA of SVM was greater than 90% for all lithologies, reporting the highest for Jatta Gypsum with 99.3% accuracy, except for the Murree Formation with 81.9% accuracy. The overall accuracy (OA) was 95.78 for SVM and 95.73 for ANN. Figure 7 shows the classified map generated by SVM and ANN, with the Jatta Gypsum deposit highlighted in orange. A previous study within the region [19] only focused on Jatta Gypsum outcrops; this study mapped nine other lithologies, including Jatta Gypsum. Ground validation of the mapped area was carried out during a field visit along a north-northeast to south-southwest transect (Figure 8).