## 4 Results and Discussions
### Validation Period  
The analysis between the observed and predicted values during the validation period was conducted in two ways. First, we analyzed the spatial variation between the observed and predicted SPEI values during January-March (Figure 8a,b), and thereafter we examined the variation in terms of drought classes. For classification purposes, we undertook a binary classification technique, by dividing the pixels into either drought or no drought. For this, all the pixels having an SPEI value less than \(-1\) were considered as drought pixels, and the remaining as non-drought pixels. The spatial variation between the observed and predicted SPEI 1 values shows that the variation in drought intensity is not significant; however, there are periods where the SPEI values depict different drought classes. Moreover, the predicted images generally show a greater number of drought pixels and a lower number of non-drought pixels compared to observed images. However, the more important thing here to note is that the clusters among them certainly remain in the same spatial domain. As an example, the observed SPEI 1 and 3 values for January 2017 have negative values toward the southeast part of the region, and the same can be seen in the case of predicted values, but the number of pixels is certainly high in the latter case. Such an observation can be made for other months, as well, for both SPEI 1 and 3 time scales. This can be considered as a good indicator of the model, but concerns regarding variation in drought class are certainly pertinent. For instance, the minimum observed SPEI 1 value during March 2018 falls under moderately dry conditions, whereas the predicted image shows the minimum value falling under severely dry conditions. Similar conditions can be found across other months, under both of the SPEI time scales. Figure 7: Variation between actual and predicted (**a**) SPEI 1 and (**b**) SPEI 3 values across all the pixels of the study region. same, with the number of predicted pixels under negative SPEI values being more than the observed pixels. (ii) There were pixels which predicted different drought classes than the observed ones, thus making it imperative to understand how many pixels were correctly predicted under each drought class. Therefore, for this purpose, Receiver Operating Characteristic (ROC) analysis [59] was used. The accuracy of the model depends on the number of correctly predicted cells for a definite drought class. The ROC curve is drawn with sensitivity on the ordinate and specificity on the abscissa [60]. The sensitivity represents the true positive ratio (TPR), and specificity represents the true negative ratio (TNR), which can be determined as follows:  
\[\text{Sensitivity}=\text{TP}/(\text{TP}+\text{FN}) \tag{6}\]  
\[\text{Specificity}=\text{TN}/(\text{FP}+\text{TN}) \tag{7}\]  
where TP is true positive, TN is true negative, FP is false positive and FN is false negative. TP and TN are the numbers of pixels that are correctly classified, whereas FP and FN are the numbers of pixels which were incorrectly classified. The area under the ROC curve (AUC) determines the model's ability for classification purposes, with values less than or equal to 0.5 indicating no better than random chance [35]. The classification was based on the seven different drought classes. During the validation period, the pixels were mostly composed of near-normal conditions, with few periods of moderately dry and severely dry conditions under both time durations. In total, out of the 36 months of the validation period, almost 70% were near normal condition, 7% each for severely dry and wet conditions, 11% and 5% for moderately dry and wet conditions, respectively. The number of pixels in each drought class was calculated for every month and categorized as drought (SPEI \(<-1\)) or non-drought (SPEI \(>-1\)), based on SPEI values. The AUC value of SPEI 1 classification was found to be 0.82, whereas SPEI 3 was found to be 0.84, which can be considered as a good performance (Figure 9).