Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh

Kafy, Abdulla Al; Faisal, Abdullah-Al-; Rahman, Md. Shahinoor; Islam, Muhaiminul; Rakib, Abdullah Al; Islam, Md. Arshadul; Khan, Md. Hasib Hasan; Sikdar, Md. Soumik; Sarker, Md. Hasnan Sakin; Mawa, Jannatul; Sattar, Golam Shabbir

doi:10.1016/j.scs.2020.102542

Cited by 127 publications

(63 citation statements)

References 41 publications

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“…For the central part of the city, the predicted and actual temperatures are approximately 293.76 K and 292.92 K respectively in winter, i.e., January, and 302.6 K and 303.06 K temperatures in summer, i.e., in May. This is in line with previous studies [68][69][70]. The authors of pertinent studies have reported that LST will increase with more constructed areas due to high built-up areas, low vegetation, and high use of anthropogenic materials [71].…”

Section: Land Surface Temperature (Lst)supporting

confidence: 91%

Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

Aslam

Maqsoom

Khalid

et al. 2021

IJGI

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Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI).

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Section: Land Surface Temperature (Lst)supporting

confidence: 91%

Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

Aslam

Maqsoom

Khalid

et al. 2021

IJGI

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“…Bangladesh is rapidly urbanizing compared to other South Asian countries, despite being predominately rural ( Muzzini and Aparicio, 2013 ; Rahman et al., 2019 ). Many city-scale studies suggest that major cities in Bangladesh are going through uncontrolled urban growth with negative environmental consequences ( Ahmed et al., 2013 ; Fattah et al., 2021a , b ; Gazi et al., 2020 ; Kafy et al., 2020 , 2021 ; Naim and Kafy, 2021 ). ( Rahman et al., 2019 ) classified 331 cities of Bangladesh using night light intensity and placed Bogura in the same cluster with seven major cities.…”

Section: Introductionmentioning

confidence: 99%

“…Urban thermal field variance index (UTFVI) is another widely used indicator derived from LST to evaluate the urban environment's eco-environmental quality or thermal well-being ( Ahmed, 2018 ; Alcantara et al., 2019 ; Alfraihat et al., 2016 ; Kafy et al., 2021 ; Maithani et al., 2020 ; Nguyen et al., 2019 ; Portela et al., 2020 ; Sharma et al., 2021 ; Sobrino and Irakulis, 2020 ). Changes in local wind patterns and humidity, declining air quality and comfort, rising mortality rates, rain, and thunderstorm activity are adverse effects caused by high UTFVI ( Kafy et al., 2021 ; Sejati et al., 2019 ; Singh et al., 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Satellite thermal infrared data are commonly utilized to derive LST with relatively high accuracy and high spatial resolution ( Mao et al., 2021 ). LST obtained from satellite imagery has been used in numerous studies of UHI and UTFVI due to the advantages of high geographic and temporal resolution, availability, and free and easy access ( Ahmed, 2018 ; Guha et al., 2017 ; Kafy et al., 2021 ; Naim and Kafy, 2021 ; Nguyen et al., 2019 ; Sharma et al., 2021 ; Wemegah et al., 2020 ). These studies examined the relationship between LST and land surface, which is considered one of the major factors in LST change.…”

Section: Introductionmentioning

confidence: 99%

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Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

Abir

Ahmmed

Sarker

et al. 2021

Heliyon

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In recent years, the world has shown considerable concerns about environmental degradation accompanied by urban expansion. In terms of size, Bogura is equivalent to most of the major cities in Bangladesh, yet no thermal and ecological assessment has ever been conducted here. This study uses multitemporal Landsat satellite images between 2001 and 2020 to investigate the thermal and ecological conditions of Bogura Sadar (sub-district). Land surface temperature (LST) is obtained from Landsat images using the widely used radiative transfer equation. The thermal and ecological conditions are evaluated by computing urban heat island (UHI) and urban thermal field variance index (UTFVI) from LST data. The influence of vegetation, built-area, water-body, and bare soil on LST are examined using land cover indices through pixel-level multivariate linear regression analysis. According to the findings of this sub-district-scale (urban and rural areas) study, the mean LST has increased by 0.62 °C in the last 20 years. As per local administrative-wise findings, LST has increased in most areas, regardless of their urban or rural function. The difference between the urban area and the rest of the surroundings was significant (1.74 °C) in 2020. In 2001, UHI affected area was 5.65 km 2 , which expanded to 8.84 km 2 in 2020. Thermal and ecological conditions are worse in urban areas than its surrounding areas. The regression models of the LST and land cover indices could explain more than half (R 2 : 0.66 to 0.73) of LST variation over the years. Land cover could explain the LST in 2020 to the least extent implying that anthropogenic activities have greater influence than earlier. Land cover could explain less than half of the LST variation in the urban area.

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“…The change in LULC substantially affects the ecosystem and biodiversity, which significantly increases the land surface temperature (LST) and accelerates climate change risks [ 7 , 8 ]. In addition, the LULC transformation progressively contributes to the arrangement of a warmer thermal condition resulting in creating the urban heat island (UHI) effect [ 9 , 10 , 11 ]. The UHI phenomenon harms social aspects, public health, and ecological sustainability [ 9 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

Kafy

Faisal

Rakib

et al. 2021

Heliyon

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Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh

Cited by 127 publications

References 41 publications

Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

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