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

may result in droughts, heatwaves, floods, and hurricanes. Thus the relative humidity is one of the important factors to measure environmental changes. Keeping in view the importance of relative humidity, the current study has attempted to predict the relative humidity in a high elevated alpine basin (Hunza) of western Karakoram by using the MARS and M5T machine learning models. The current study is novel in that respect that previously nobody tried to predict the relative humidity in a high elevation alpine basin.

Statistical analysis of the model outputs suggested that both the models produced reliable predictions of relative humidity at Khunjerab, Naltar, and Ziarat meteorological stations of the Hunza basin during both training and testing stages. Out of 10 input data combinations of temperature, precipitation, and relative humidity, the 6th combination (i.e. RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3) produced the best results for each station by each model. The statistical indicators confirmed the excellent performance of both the models at all stations. For the MARS model, RMSE, MAE, and R2 values ranged from 5.26–5.63%, 4.51–4.59%, and 0.826–0.856, respectively, during the training stage while they ranged from 5.86–6.58%, 4.97– 5.43%, and 0.806–0.815, respectively, during the testing stage. However, in the case of the M5T model, the RMSE, MAE, and R<sup>2</sup> values ranged from 5.74–5.94%, 5.04– 2.12%, and 0.791–0.796, respectively, during the training stage whereas the values ranged from 6.08–6.19%, 5.46–5.58%, and 0.762–0.783, respectively, during the testing stage of M5T model. Both the models showed poor performance such as (R<sup>2</sup> <0.50) in the case of S1, S2, and S3 input combinations at all stations. Moreover, it was observed that both the models performed better in training as compared to the testing stage. Both the models outperformed at Ziarat as compared to other stations. Overall, the MARS model performed better than M5T at all stations. The current study is important and it will provide a baseline for future studies to predict the other meteorological variables such as temperature, wind speed, solar radiation, and evapotranspiration by using machine learning tools in high altitude and remote basins which face the issue of data scarcity.
