Forecasting Land Surface Temperature in Kathmandu Using Univariate and Multivariate Time Series Models

Rapid growth and domestic migration to capital city, Kathmandu Metropolitan City (KMC), Nepal have amplified land-use transition to unmanaged and unplanned urbanization have exacerbated problems of Urban Heat Island (UHI) Effect. Increasing built up surfaces, means of transport and industrial activities are major results for increasing temperature in the city area as compared to other areas. This study focuses on forecasting Land Surface Temperature (LST) using both univariate and multivariate time series models. The primary goal is to predict the impact of climatic factors on the Urban Heat Island (UHI) effect, utilizing data from 1981 to 2019. We analyzed key factors including - (i) relative humidity, (ii) surface pressure, (iii) specific humidity, and (iv) wind speed sourced from the MEERA-2 dataset. A univariate model using exponential smoothing achieved a Mean Absolute Error (MAE) of 3.17°C and an R² score of 0.94, highlighting strong predictive accuracy. Additionally, the multivariate Prophet model, which incorporates trend and seasonal components, further refined the forecasting by integrating multiple climatic factors. This study aims provides valuable information via prediction for sustainable urban planning and landscape design in KMC and other 61 districts.