Assessing the Spatial Distribution of Urban Heat Island in Erdenet City Using Machine Learning Approaches
DOI:
https://doi.org/10.5564/mjgg.v62i46.4314Keywords:
Urban Heat Island, Machine Learning, Land Surface Temperature, Remote Sensing, Spatial AnalysisAbstract
The Urban Heat Island (UHI) phenomenon represents a critical ecological challenge associated with rapid global urbanization, exerting adverse impacts on energy consumption, air quality, and public health. As Mongolia's major mining city, Erdenet is experiencing increasing environmental pressures during its urbanization process, necessitating comprehensive investigation of this phenomenon. This study aimed to analyze the spatiotemporal variations of UHI effects in Erdenet city over a 35-year period (1989-2024) and develop predictive models for future projections. Utilizing multi-temporal Landsat satellite imagery for spatially modeling Land Surface Temperature (LST), advanced machine learning algorithms including Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were employed. The results revealed that mean LST increased from 23.05°C in 1989 to 31.15°C in 2004, subsequently decreasing to 25.70°C in 2024. A notable finding was the spatial redistribution of UHI patterns: during the initial years, elevated LST values were concentrated in areas with sparse vegetation cover and barren land, while by 2024, high temperatures had shifted to impervious surfaces including built-up areas and residential settlements. This spatial transition indicates an intensification of UHI effects driven by urban expansion. Throughout the study period, forest area decreased significantly, while water bodies and green infrastructure consistently maintained lower temperatures, demonstrating the crucial temperature-regulating role of these natural elements. These findings align with international research emphasizing the vital role of vegetation in mitigating UHI intensity, and highlight the imperative to integrate green infrastructure into urban planning to enhance climate resilience in rapidly urbanizing regions.
Эрдэнэт хотын “Хотын дулааны арал”-ын орон зайн тархалтыг машин сургалтын аргаар үнэлэх нь
ХУРААНГУЙ: “Хотын дулаан арал”-ийн (Urban Heat Island, UHI) үзэгдэл нь дэлхийн хурдацтай хотжилттой холбоотой гол экологийн асуудлын нэг бөгөөд эрчим хүчний хэрэглээ, агаарын чанар болон иргэдийн эрүүл мэндэд сөрөг нөлөөг үзүүлж байна. Монгол Улсын томоохон уул уурхайн хот Эрдэнэт нь хотжих явцдаа ийм төрлийн экологийн дарамт үүсэж байгаа тул энэ үзэгдлийг судлах шаардлага зайлшгүй гарч ирлээ. Энэхүү судалгаанд Эрдэнэт хотын UHI үзэгдлийн орон зайн өөрчлөлтийг 35 жилийн хугацаанд (1989-2024) шинжилж, цаашдын урьдчилсан таамаглал гаргах зорилго тавьсан. Олон цаг хугацааны Landsat хиймэл дагуулын мэдээллийг ашиглан газрын гадаргын температур (LST)-ийг орон зайд загварчлахдаа ахисан түвшний машин сургалтын Random Forest (RF) болон Extreme Gradient Boosting (XGBoost) алгоритмуудыг хэрэглэлээ. Судалгааны үр дүнгээс харахад LST-ийн дундаж утга 1989 онд 23.05°C байсан бол 2004 онд 31.15°C хүртэл өссөн ч 2024 онд 25.70°C болж буурчээ. Онцлох зүйл нь UHI-ийн орон зайн шилжилт юм: эхний жилүүдэд LST-ийн өндөр утга нь ургамлын бүрхэвч сийрэг, халцгай газарт төвлөрч байсан бол 2024 онд барилга байгууламж, гэр хороолол зэрэг ус үл нэвтрэх гадаргуу бүхий газруудад тэлжээ. Энэ нь хотын тэлэлтээс шалтгаалан UHI-ийн эрчим нэмэгдэж буйг илтгэнэ. Судалгааны хугацаанд ойн сангийн талбай эрс багассан харин усны сан, ногоон байгууламж бүхий газрууд тогтмол бага температуртай байсан нь эдгээр байгалийн элементүүдийн температур зохицуулах чухал үүргийг харуулж байна. Эдгээр үр дүн нь UHI-ийн эрчмийг бууруулахад ургамлын чухал үүргийг онцолсон олон улсын судалгаатай нийцэж, хурдацтай хотжиж буй бүс нутагт уур амьсгалын тэсвэртэй байдлыг нэмэгдүүлэх зорилгоор ногоон дэд бүтцийг хот төлөвлөлтөд нэгтгэх шаардлагыг харуулж байна.
Түлхүүр үгс: Хотын дулааны арал, Машин сургалт, Газрын гадаргын температур
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Copyright (c) 2025 Byambadolgor Batdorj, Jargaldalai Enkhtuya, Amarsaikhan Damdinsuren, Sainbayar Dalantai, Munkh-Erdene Altangerel, Gantsetseg Gantumur
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