Abstract
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).
Highlights
long short-term memory (LSTM) addresses the problems of recurrent neural networks (RNNs) effectively and represents the short and long-term dependencies more effectively. These LSTM models have been employed successfully in studies on time-series data in different disciplines, such as speech recognition, machine translation, and traffic flow forecasting [53]. The use of these models to study urban overheating and associated Urban Heat Island effects (UHI) effect and Land Surface Temperature (LST) of areas has not been reported yet, presenting a gap targeted in the current study
The proposed LSTM model has been compared with the previously used Support Vector Regression (SVR) and Artificial Neural Network (ANN) models to highlight the increase in the accuracy and efficiency of prediction
The results show that Enhanced Vegetation Index (EVI) for January ranges from −0.0457 to 0.4437 while that for May ranges from −0.0297 to 0.3464
Summary
Human activities and excessive construction have altered the surface structure and materials of the Earth, thereby disturbing the natural environment. These changes disturb the Earth’s surface energy balance and the atmospheric composition, disturbing the local climate. Urbanization, one of the major human activities, transform the natural landscape into an impervious and built-up area. The impervious area consists of materials such as bricks, concrete, bitumen, tiles, and roof sheeting, creating a built environment [2]. The human population is rapidly shifting towards a comfortable urban lifestyle. This is due to better job opportunities and improved quality of life in the urban areas. The world’s urban population that was merely about
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