Matches in SemOpenAlex for { <https://semopenalex.org/work/W4315652177> ?p ?o ?g. }
- W4315652177 endingPage "1416" @default.
- W4315652177 startingPage "1416" @default.
- W4315652177 abstract "Urbanization-led changes in land use land cover (LULC), resulting in an increased impervious surface, significantly deteriorate urban meteorological conditions compromising long-term sustainability. In this context, we leverage machine learning, spatial modelling, and cloud computing to explore and predict the changing patterns in urban growth and associated thermal characteristics in Bahawalpur, Pakistan. Using multi-source earth observations (1990–2020), the urban thermal field variance index (UTFVI) is estimated to evaluate the urban heat island effect quantitatively. From 1990 to 2020, the urban area increased by ~90% at the expense of vegetation and barren land, which will further grow by 2050 (50%), as determined by the artificial neural network-based prediction. The land surface temperature in the summer and winter seasons has experienced an increase of 0.88 °C and ~5 °C, respectively. While there exists spatial heterogeneity in the UTFVI 1990–2020, the city is expected to experience a ~140% increase in areas with severe UTFVI in response to predicted LULC change by 2050. The study provides essential information on LULC change and UTFVI and puts forth useful insights to advance our understanding of the urban climate, which can progressively help in designing more livable and sustainable cities in the face of environmental changes." @default.
- W4315652177 created "2023-01-12" @default.
- W4315652177 creator A5034160184 @default.
- W4315652177 creator A5041270166 @default.
- W4315652177 creator A5071495414 @default.
- W4315652177 creator A5072733130 @default.
- W4315652177 date "2023-01-11" @default.
- W4315652177 modified "2023-09-29" @default.
- W4315652177 title "Towards Sustainable and Livable Cities: Leveraging Remote Sensing, Machine Learning, and Geo-Information Modelling to Explore and Predict Thermal Field Variance in Response to Urban Growth" @default.
- W4315652177 cites W1924218178 @default.
- W4315652177 cites W1966878029 @default.
- W4315652177 cites W1967663794 @default.
- W4315652177 cites W1990793870 @default.
- W4315652177 cites W2022555621 @default.
- W4315652177 cites W2063623478 @default.
- W4315652177 cites W2082291024 @default.
- W4315652177 cites W2084668217 @default.
- W4315652177 cites W2087729435 @default.
- W4315652177 cites W2101678239 @default.
- W4315652177 cites W2129457558 @default.
- W4315652177 cites W2160332013 @default.
- W4315652177 cites W2167787089 @default.
- W4315652177 cites W2515646680 @default.
- W4315652177 cites W2531688208 @default.
- W4315652177 cites W2591607720 @default.
- W4315652177 cites W2605399805 @default.
- W4315652177 cites W2735326695 @default.
- W4315652177 cites W2742225967 @default.
- W4315652177 cites W2753822254 @default.
- W4315652177 cites W2766529052 @default.
- W4315652177 cites W2773259008 @default.
- W4315652177 cites W2807007945 @default.
- W4315652177 cites W2886877128 @default.
- W4315652177 cites W2889951441 @default.
- W4315652177 cites W2907470085 @default.
- W4315652177 cites W2908960804 @default.
- W4315652177 cites W2913429597 @default.
- W4315652177 cites W2946146482 @default.
- W4315652177 cites W2947066650 @default.
- W4315652177 cites W2947287914 @default.
- W4315652177 cites W2965661103 @default.
- W4315652177 cites W2972199278 @default.
- W4315652177 cites W2992406221 @default.
- W4315652177 cites W2998209783 @default.
- W4315652177 cites W2999096842 @default.
- W4315652177 cites W3003793110 @default.
- W4315652177 cites W3011655579 @default.
- W4315652177 cites W3014372673 @default.
- W4315652177 cites W3016008631 @default.
- W4315652177 cites W3024400674 @default.
- W4315652177 cites W3034892684 @default.
- W4315652177 cites W3045900364 @default.
- W4315652177 cites W3047187228 @default.
- W4315652177 cites W3047317383 @default.
- W4315652177 cites W3080319602 @default.
- W4315652177 cites W3093135203 @default.
- W4315652177 cites W3093701503 @default.
- W4315652177 cites W3106753127 @default.
- W4315652177 cites W3113176042 @default.
- W4315652177 cites W3120772301 @default.
- W4315652177 cites W3134927562 @default.
- W4315652177 cites W3135597908 @default.
- W4315652177 cites W3153297155 @default.
- W4315652177 cites W3154204948 @default.
- W4315652177 cites W3161377684 @default.
- W4315652177 cites W3174050889 @default.
- W4315652177 cites W3176865099 @default.
- W4315652177 cites W3182553424 @default.
- W4315652177 cites W375108284 @default.
- W4315652177 cites W4239711993 @default.
- W4315652177 cites W4243817694 @default.
- W4315652177 cites W4289261981 @default.
- W4315652177 doi "https://doi.org/10.3390/su15021416" @default.
- W4315652177 hasPublicationYear "2023" @default.
- W4315652177 type Work @default.
- W4315652177 citedByCount "3" @default.
- W4315652177 countsByYear W43156521772023 @default.
- W4315652177 crossrefType "journal-article" @default.
- W4315652177 hasAuthorship W4315652177A5034160184 @default.
- W4315652177 hasAuthorship W4315652177A5041270166 @default.
- W4315652177 hasAuthorship W4315652177A5071495414 @default.
- W4315652177 hasAuthorship W4315652177A5072733130 @default.
- W4315652177 hasBestOaLocation W43156521771 @default.
- W4315652177 hasConcept C100970517 @default.
- W4315652177 hasConcept C107826830 @default.
- W4315652177 hasConcept C119857082 @default.
- W4315652177 hasConcept C121955636 @default.
- W4315652177 hasConcept C127413603 @default.
- W4315652177 hasConcept C132651083 @default.
- W4315652177 hasConcept C144133560 @default.
- W4315652177 hasConcept C147176958 @default.
- W4315652177 hasConcept C153083717 @default.
- W4315652177 hasConcept C153294291 @default.
- W4315652177 hasConcept C158049464 @default.
- W4315652177 hasConcept C162324750 @default.
- W4315652177 hasConcept C166957645 @default.
- W4315652177 hasConcept C17744445 @default.
- W4315652177 hasConcept C18903297 @default.