Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India

Mohammad, Pir; Goswami, Ajanta; Chauhan, Sarthak; Nayak, Shailesh

doi:10.1016/j.uclim.2022.101116

Cited by 59 publications

(19 citation statements)

References 56 publications

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“…The XGB regression model can calculate the optimal solution for the whole model and reduce the overfitting phenomenon [49]. Notably, previous study has confirmed that the XGB regression model outperforms random forest, support vector, and decision tree regressions with higher accuracy in LST prediction [50]. Moreover, the XGB regression model has shown good application effects in many fields, including crime prediction [47], vegetation mapping [51], algal biochar yield prediction [52], flood susceptibility modeling [53], and urban thermal environment [54].…”

Section: Application Of Regression Model To Analyze Correlation Betwe...mentioning

confidence: 95%

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

et al. 2022

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Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban heat island effect. Previous studies have focused on the correlation between LST and the environmental factors that drive its formation, without considering the influences of the neighboring environment and the vertical expansion of highly urbanized areas. Notably, the correlation between LST and its neighboring environment in different seasons remains unclear. In this study, we selected central Beijing in China as our study area and employed the moving window method to characterize the environmental factors of the neighboring environment of the central LST cell. We explored eight environmental factors from three layers: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI), building density (BD), building height (BH), building volume (BV), sky view factor (SVF), and road density (RD). The Pearson correlation and extreme gradient boosting (XGB) regression methods were applied to measure the correlation between LST and the different factors in moving windows of different sizes. The results indicated that the correlation between NDVI, MNDWI, and LST was considerably different in the winter and other seasons. However, NDBI was positively correlated with LST in all seasons, although the correlation was strongest/weakest in summer/winter. Among building-related factors, BD and BH were more strongly correlated with LST, and the positive/negative correlation between BD/BH and LST was stronger in summer/winter. The correlation between LST and its neighboring environment varied with increasing window size, and this variation differs significantly between winter and other seasons. In spring, summer, and autumn, the strength of the correlation between LST and its neighboring environment showed an “inverted V” pattern with increasing window size. The optimal spatial scales to explore the influence of neighboring environments on the LST of 30-m cells were 210 m and 270 m. This study revealed the seasonal correlation between LST and its neighboring environment while explaining the variation at a spatial scale. Notably, this study can provide a new perspective for understanding the driving mechanism of the urban thermal environment, while contributing to its scientific optimization and management.

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Section: Application Of Regression Model To Analyze Correlation Betwe...mentioning

confidence: 95%

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

et al. 2022

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“…In order to acquire the LST over Dehradun using an artificial neural network, Maithani et al, (2022) employed built-up densities with a mean absolute error of 1.5° C and 0.9° C, while Rana and Suryanarayana (2022) employed four ML techniques, K nearest neighbour, neural network, regression tree, support vector machine incorporating three indices resulting with an RMSE of 0.54° C, 0.59° C, 0.89° C, and 0.61° C respectively. Mohammad et al, (2022) predicted the LST over a city in Ahmedabad with an RMSE of 0.03° C using XGB regressor.…”

Section: Introductionmentioning

confidence: 99%

A Generic Machine Learning-Based Framework for Predictive Modeling of Land Surface Temperature

Rengma

Yadav²

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the realm of data analytics, machine learning (ML) is one of the most successful techniques for making predictions. The ability of ML algorithms has also been studied in various aspects of land surface temperature (LST) besides its retrieval. The few investigations on LST retrieval using ML algorithms suggested that it may potentially obtain the LST values incorporating relevant variables of land surface parameters; however, the variables and ML models used differ, and so do their accuracies. The accuracy of the model is affected by the variable's contribution, its quality and quantity, and the fulfilment of each technique's assumptions. Hence this study provides a wide range of LST indicators to be employed for LST retrieval using a widely used ML algorithm, random forest. The ML algorithm framework for LST prediction is illustrated with significant spectral indices and terrain parameters across the highly industrialised district of Jharkhand, India. With the exception of one (aspect) variable, the analysis shows that all 20 variables that were included as independent factors were significant and equally contributed to the model. The model built with all the variables including the aspect of the terrain obtained an RMSE of 1.13 degree Celsius and R2 of 0.48. However, after the removal of aspect, the model obtained an R2 of 0.89 and RMSE of 0.74 °C. The performance of the model on consecutive removal of lesser significant variables are evaluated and the study made clear how crucial it is to consider several environmental or land-use factors that could be pertinent to LST.

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“…Confusing the kappa coefficient of the matrix is an effective verification method (Phan et al 2020 ; Mohammad et al 2022 ). Therefore, the kappa coefficient was applied to verify the accuracy of land use change prediction models.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

Wang

Yin

Liu

et al. 2023

Environ Sci Pollut Res

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The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km2, 271.7136 km2, 40.0104 km2, and 109.2483 km2, respectively, whereas grassland and bare land decreased by 677.7243 km2 and 598.5945 km2, respectively; (2) Kappa coefficients reflect the accuracy of the mode’s predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA–Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources.

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Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India

Cited by 59 publications

References 56 publications

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

A Generic Machine Learning-Based Framework for Predictive Modeling of Land Surface Temperature

Spatiotemporal change and prediction of land use in Manasi region based on deep learning

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