PREDICTIVE MODEL TO MINIMIZE THE EFFECT OF EXTREME TEMPERATURE IN SKARDU AND ASTORE, GILGIT BALTISTAN

Abstract

Climate is a fundamental factor of the natural environment that has a role in both natural and human existence. Temperature is an important climatic element that influences snow melting, evaporation, and frost directly. Current study has used Mean Monthly Minimum Temperature (MMMT) of Skardu from 1972 to 2021 and of Astore from 1993 to 2021 based on the availability of data. In this work; we have used SARIMA (Seasonal Auto Regressive Integrated Moving Average Model) to forecast mean monthly minimum temperature. Skardu data is stationary at level form, which suggests SARMA model for Skardu station and Astore data is stationary at first difference so SARIMA time series is appropriate for mean monthly minimum temperature of Astore. Using Box Jenkins’s approach it is found that the most appropriate model for Skardu is SARMA(1,0)(1,0)12 and Astore is SARIMA (0,1,1)(4,1,4)12 respectively. These models have been utilized to forecast MMMT from 2022 to 2036. Yearly mean minimum temperature forecasts show that the mean minimum temperature at Skardu and Astore stations is slightly decreasing. The yearly mean minimum temperature at Skardu station is 4.0 °C in 2022 and will decrease to 2.3 °C till 2036, while at Astore station it is 4.0 °C in 2022 and will fall to 2.6 °C in 2036. Our results will be useful for decision makers and insurance companies for better future planning to minimize the effect of lowest temperature.