**1. Introduction**

The relative humidity is defined as the amount of water vapor in the air in comparison with the full saturation [1, 2]. Being the important indicator of precipitation forecasting, its prediction plays a significant part in improving the accuracy of weather forecasting [3]. The relative humidity changes with respect to change in saturated vapor pressure which further depends on wind speed, solar radiation, pressure, temperature, and moisture content in the air [1]. The relative humidity is a function of temperature and is regarded as a sensitive parameter in the field of science [4]. Relative humidity plays a vital role in plant growth, agricultural and industrial production and in the prevention and control of air pollution [5];

economic stability of a region, water systems and also in managing renewable and solar energy systems [1, 6], weather and climate [7, 8]. Moreover, it has also an impact on ozone concentration and adaptive thermal comfort [9]. Keeping in view the importance of relative humidity, the research on its prediction is increasingly important [7].

The relative humidity is an important aspect of the hydrological phase [8] and has a role in alpine hydrology, especially, in a cold and dry climate; any change in temperature and humidity causes larger variations in the ablation of glaciers [10]. The warm environment glaciers are subjected to be influenced more by the change in relative humidity. Few other studies e.g. [11–13] also observed that tropical glaciers are sensitive to subtle changes in relative humidity, precipitation, and cloudiness. Relative humidity and clouds play an important role in the energy balance of glaciers by controlling the number of outgoing longwave radiation. Moreover, relative humidity and wind speed influence the turbulent latent heat flux which supplies all energy for sublimation and thus they indirectly control the equilibrium line altitude (ELA) [14]. Another study conducted by [15] observed that relative humidity has an effect on evaporation and there is an inverse relation between them. Evaporation further controls the water balance of closed lakes in hilly areas and evapotranspiration, especially, in irrigated agricultural areas.

Regardless of relative humidity is an important component of hydrology, meteorology, and climate, only a few studies are available for its prediction. A study conducted by [1] used artificial neural networks (ANNs) and genetic expression programming (GEP) models for the prediction of relative humidity as a function of three meteorological variables: wind speed, temperature, and pressure in two Californian gauging stations. They observed that both the models can successfully predict one-year relative humidity data into the future. Another study done by [5] predicted relative humidity by establishing time series models such as Extreme Gradient Boosting (XGBoost), Seasonal Auto-Regressive Integrated Moving Average (SARIMA), and Holt-Winters (HW). The XGBoost was found more accurate because of its robust capability to resist a fitting. The study conducted by [3] found that the performance of an autoregressive integrated moving average (ARIMA) model is better than the Long Short-Term Memory (LSTM) Network for the prediction of relative humidity. On contrary, [8] observed that the LSTM network is capable of predicting complex univariate relative humidity time series with robust no-stationarity. However, Least Square Support Vector Machine (LSSVM) and Adaptive Network-Based Fuzzy Inference System (ANFIS) models were used by [2] for prediction of relative humidity in terms of dry bulb temperature and wet bulb depression and found satisfactory.

Another study conducted by [16] proposed four ANNs models to predict the relative humidity and temperature in a swine livestock warehouse located in Puerto Gaitan–Meta. They observed that the models used in the study are suitable for the prediction of humidity in barns not equipped with humidity sensors. However, [17] used an improved backpropagation (BP) neural network for the prediction of indoor relative humidity and temperature every 10 min and 6–72 hours in advance based on a cloud database in Chongqing, China. Both temperature and humidity predictions have a strong correlation with the observed data. Similarly, another study conducted by [18] used BP neural network for the prediction of one day ahead mean air temperature and relative humidity of greenhouse located in the subhumid sub-tropical regions of India. The results displayed that the BP neural network model provided the best prediction for inside temperature and relative humidity. However, a study done by [19] used daily minimum air temperature (*T*n) downscaled from INMCM4 general circulation model (GCM) to predict the relative humidity for climate change studies but relative humidity predictions were poor in

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

few months especially in March, July, August, and October. Moreover, a study conducted by [20] proposed a Functional Link Neural Network (FLNN) which comprises of a single layer of tunable weight trained with the Modified Cuckoo Search algorithm (MCS) for prediction of daily temperature and relative humidity. It was observed that FNN when trained with MCS produced less prediction error. Further, an attempt has been made for the prediction of relative humidity and temperature at different locations inside tobacco dryer by [21] by using a fitting ANN model. Another study performed by [22] also used different ANN models to successfully forecast indoor relative humidity and temperature in the education building of Izmir, Turkey.

Formerly, no attempt has been made for the prediction of relative humidity in the alpine catchment where there is an issue of data scarcity. The current study is unique because it uses two machine learning models such as MARS and M5T to predict the relative humidity in the Hunza basin (glaciated basin), Pakistan. MARS model was selected because it requires a short training process and has the ability to model complex nonlinear processes deprived of strong model assumptions as compared to ANNs models [23, 24] whereas the M5T model was selected because of its small computation cost and ease in large data treatment as compared to support vector machine (SVM) and ANN [25, 26]. In previous studies, mostly these models were used for the prediction of runoff in poorly gauged basins. A study conducted by [27] suggested that the MARS method is capable of predicting short-term runoff forecast in mountainous watersheds whereas MARS was successfully used for the prediction of streamflows with inadequate data input in the mountainous catchment by [28]. Similarly, the M5T model was found useful in the prediction of streamflows of several tributaries by [29] and it was observed that predictions are good in rainless periods. Another study conducted by [30] found the M5T algorithm reliable in the prediction of streamflows. Several other studies also encouraged the researchers to use MARS and M5T models for the prediction of runoff e.g. [31–37]. Apart from runoff prediction, MARS and M5T models were also used for the prediction of evapotranspiration (ET) and Pan Evaporation (Ep). A study conducted by [38] compared the performance of M5T, MARS along with calibrated Hargreaves-Samani (CHS), MLP, and Stephens-Stewart (SS) models and observed that MARS performed better in the prediction of Ep. Another study conducted by [39] found that the M5T model outperformed compared to Ritchie Equation for the prediction of ET. Similarly, [40] successfully predicted reference evapotranspiration by using M5T and ANN models.
