Preprint
Article

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

Altmetrics

Downloads

53

Views

58

Comments

0

This version is not peer-reviewed

Submitted:

25 November 2024

Posted:

26 November 2024

You are already at the latest version

Alerts
Abstract
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 UHI effect, utilizing data from 1981 to 2019 and understand which model best serves as key to prediction for LST. We analyzed key factors including - (i) relative humidity, (ii) surface pressure, (iii) specific humidity, and (iv) wind speed sourced from the MEERA-2 dataset. This study aims provides valuable information via prediction for sustainable urban planning and landscape design in KMC.
Keywords: 
Subject: Computer Science and Mathematics  -   Artificial Intelligence and Machine Learning
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

Subscribe

© 2024 MDPI (Basel, Switzerland) unless otherwise stated