**Table 7.**

*The statistical evaluation of the M5T model at Naltar.*


#### **Table 8.**

*The statistical evaluation of the M5T model at Ziarat.*

meteorological stations both during training and testing stages; however, it provided the best predictions of relative humidity for the 6th input data combination (S6) at all stations which are highlighted in bold. Overall, the M5T model performance was slightly lower as compared to MARS. The M5T model also performed better during training as compared to testing at all meteorological stations. However, the M5T model provided the best prediction of relative humidity at Ziarat as compared to Naltar and Khunjerab (**Table 8**). However, the M5T model did not perform well for the prediction of relative humidity for the S1, S2, and S3 scenarios with R2 <0.50 at all meteorological stations (**Tables 6**–**8**). A previous study conducted by [8] observed that the LSTM model is capable of forecasting complex univariate relative humidity time series. On contrary, [3] suggested that ARIMA can provide a better prediction of relative humidity as compared to LSTM.

At Khunjerab station, the M5T model performed well (RMSE= 5.94%, MAE = 5.08%, R<sup>2</sup> = 0.796) in case of S6 input combination during model training stage whereas it displayed low prediction performance (RMSE= 6.14%, MAE= 5.56%, R2 = 0.772) during testing stage as shown in **Table 6**. Similarly, the M5T model did not perform well for the S1, S2, and S3 scenarios (R2 <0.50). Similarly, at Naltar station, the M5T model performed reasonably well (RMSE= 5.82%, MAE= 5.12%**,** R2 = 0.791) for S6 input combination during training stage whereas it exhibited a slightly low performance (RMSE= 6.19%, MAE= 5.58%, R2 = 0.762) during testing stage as presented in **Table 7**.

However, the M5T model provided the best prediction of relative humidity at the Ziarat station for the S6 input combination (**Table 8**). The M5T model performed better during training (RMSE= 5.74%, MAE= 5.04%, R2 = 0.796) as compared to testing (RMSE= 6.08%, MAE= 5.46%, R2 = 0.783) stage as displayed in **Table 8**.

The M5T model performance was also evaluated by drawing scatter plots. The scatter plots were drawn between observed and predicted relative humidity from 2007 to 2009 on daily data as displayed in **Figure 4**. Scatter plots showed that, the M5T model can also predict relative humidity fairly well at all meteorological stations, especially, at Ziarat (R2 = 0.782) for the S6 input combination during the testing stage (**Figure 4**).

*Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using… DOI: http://dx.doi.org/10.5772/intechopen.98226*
