Time Series Analyses and Forecasting of Surface Urban Heat Island Intensity Using ARIMA Model in Punjab, Pakistan

Abstract

In the context of rapid urbanization, Urban Heat Island (UHI) is considered as a major anthropogenic alteration in Earth environments, and its temporal trends and future forecasts for large areas did not receive much attention. Using land surface temperature (LST) data from MODIS (Moderate Resolution Imaging Spectro-radiometer) for years 2006 to 2020, we quantified the temporal trends of daytime and nighttime surface UHI intensity (SUHII, difference of urban temperature to rural temperature) using the Mann-Kendall (MK) trend test in six major cities of the Punjab province of Pakistan and estimated the future SUHII for the year 2030 using the ARIMA model. Results from the study revealed that the average mean SUHII for daytime was noted as 2.221 °C and the average mean nighttime SUHII was noted as 2.82 °C for the years 2006 to 2020. The average mean SUHII for daytime and nighttime exhibited increasing trends for all seasons and annually, and for the daytime spring season it showed a maximum upward trend of 0.486 °C/year (p < 0.05) and for the nighttime annual SUHII with an increasing rate of 0.485 °C/year (p < 0.05) which exhibited a maximum upward trend. The ARIMA model forecast suggested an increase of 0.04 °C in the average daytime SUHII and an increase of 0.1 °C in the average nighttime SUHII until 2030. The results from this study highlight the increasing trends of daytime and nighttime SUHII, ARIMA also forecasted an increase in daytime and nighttime SUHII, suggesting various strategies are needed for an effective mitigation of the UHI effect.

Spatiotemporal analysis of urban expansion in Srinagar city, Kashmir

“…Urbanization is a socio-cultural phenomenon driven by complex social, economic, and political factors, as well as the geographical features of a region, and is an important demographic and spatial trend around the world [1,2]. This has led to the loss of agricultural and forest lands, the emergence of barren areas, an increase in impermeable surfaces, a reduction in green cover, and the expansion of built-up areas, contributing to the Land Use and Land Cover (LULC) changes and rise in Land Surface Temperature (LST) [3][4][5].…”

“…Sparse forest area in Srinagar city was 0.76 km2 in 2000, which has reduced to 0.7 km 2 in 2020. The percentage of area covered by sparse forest was 0.29% in 2000, which has reduced to 10.14% in 2020.…”

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

“…The high LST concentration is primarily influenced by both horizontal and vertical expansion (Crum and Jenerette, 2017), the space between buildings, building material (Faroughi et al, 2020;Sadiq Khan et al, 2020;Song et al, 2020), landscape composition, and topographic parameters (Peng et al, 2017;Bera et al, 2022) among other factors. Also, the geographical location and seasonal variations play important roles in increasing LST, resulting in the creation of Urban Heat Islands (UHIs) (Khan I. et al, 2019;Guo et al, 2020;Bera et al, 2022;Tariq et al, 2022a;Mehmood et al, 2023). These geographical locations/places near the equator receive more radiation and thus, are more susceptible to the formation of UHIs, which is directly linked to high energy consumption, air pollution, and risks to human health (Shahmohamadi et al, 2011;Heaviside et al, 2017).…”

“…Although some previous studies have focused on the historical changes and future LULC scenarios in Pakistan and worldwide (Bokaie et al, 2016;Heaviside et al, 2017;Ullah et al, 2019a;Amir Siddique et al, 2020;Imran and Mehmood, 2020;Kafy et al, 2020;Sadiq Khan et al, 2020;Tariq and Shu, 2020;Arshad et al, 2022;Bera et al, 2022;Tariq and Mumtaz, 2022;Waleed and Sajjad, 2022;Mehmood et al, 2023;Zafar et al, 2023), research on LULC and LST modeling, particularly under historical and future scenarios, is rare to find. This situation of absence of information on LULC transitions, their association with LST, and future dynamics of both hinder informed planning of urban regions in Pakistan in the face of environmental changes-representing a potential domain of research that requires the attention of researchers.…”

Word game prediction and difficulty classification based on machine learning

“…An increasing number of researchers are using machine learning (ML) to address prediction and classification problems. researchers such as Yanrui Ning, Mohammed Achite, MS Mehmood, and SJ Parreño [2][3][4][5] have utilized ARIMA models to conduct real-time quantitative predictions on various research topics, capturing future trends in these subjects. Additionally, researchers like E Eskandari, Bo Li, Sangeeta Sangeeta, and Dini Rohmayani [6][7][8][9] have employed prediction or classification techniques on different research subjects and have drawn experimental conclusions by comparing classification results.…”