臺灣位於炎熱潮濕的副熱帶氣候區，都市化發展、建築熱排放及人類活動導致都市熱島效應（Urban Heat Island, UHI），使都市氣溫顯著高於周邊郊區。本研究以臺中市以及雙北市為案例，利用高解析度地理氣候數據（High-resolution Atmosphere Modeling - Weather Research and Forecasting Model, HiRAM-WRF）探討UHI的成因。經由地理資訊系統（Geospatial Information Systems, GIS）進行網格化空間數據分析，並計算生理等效溫度（Physiological Equivalent Temperature, PET）指數以識別熱區，研究發現，環境因子對都市氣溫具有複雜且非線性的影響，因此分析具有挑戰性。
本研究於臺中市區域採用決策樹演算法辨識不同開發程度區域的關鍵環境因子，並進行權重分析以選擇有效降溫策略。最後，利用倒傳遞神經網路（Back Propagation Neural Network, BPNN）模型模擬不同開發程度下的降溫政策並驗證其效能。另外於雙北市區域中，利用極端梯度提升演算法（eXtreme Gradient Boosting, XGBoost），以HiRAM-WRF模型提供5公里乘以5公里之網格解析度的廣域氣溫模擬，掌握宏觀氣候變化與都市熱島效應的空間分布，加以結合中央氣象站數據具備高時空解析度，細緻反映都市中不同區域的實際溫度變化，此跨尺度溫度預測模型，於人為開發強度高的地區更具代表性，也更能準確預估該區域的建築能耗預估情況。
此外，機器學習方法在都市熱島研究中的應用，已被許多研究證實能有效辨識出影響熱環境的致熱因子，並能量化其在不同季節、不同空間尺度下的影響力。本研究於臺中、雙北等大都市的實證研究中，不同的機器學習模型皆顯示，每增加10%的水綠空間，能顯著降低市區空氣溫度約0.22至0.31℃，而建築密度與不透水面積每增加10%，則會加劇熱島效應，空氣溫度將上升約0.27至0.37℃，另外，經由結合氣象觀測數據(Climate Observation Data Inquire System, CODiS)數據進行校正，本研究大幅提升了溫度預測的準確性，XGBoost模型預測誤差率約在10%以內，R²的範圍落於0.73至0.77之間，平均RMSE值則為1.2-1.5°C，MAE值為1.0-1.2°C，顯著優於傳統線性分析方法。最後，這些成果對於高密度都市區的熱島治理與政策制定，提供了科學依據和決策支持。未來可結合更都市形態資料與長期氣候監測數據，進一步優化模型，提升都市熱環境的降溫策略與科學性規劃，為臺灣及全球類似氣候區都市的永續發展貢獻重要參考。

Taiwan’s hot, humid subtropical climate, combined with urbanization, building heat emissions, and human activities, has intensified the Urban Heat Island (UHI) effect, causing urban temperatures to rise significantly above those of surrounding suburbs. This study examines UHI in Taichung and Taipei using high-resolution atmospheric modeling (HiRAM-WRF) and grid-based spatial analysis via Geospatial Information Systems (GIS), alongside the Physiological Equivalent Temperature (PET) index to identify heat-prone zones. Results show that environmental factors have complex, nonlinear impacts on urban temperatures. For Taichung, decision tree algorithms identified key environmental variables in areas with different development intensities, and weight analysis selected effective cooling strategies. A Back Propagation Neural Network (BPNN) simulated and validated cooling policy outcomes under various scenarios. In Taipei, the eXtreme Gradient Boosting (XGBoost) algorithm, combined with HiRAM-WRF, provided temperature simulations at a 5 km grid, capturing macro-level UHI distribution. Integration with high-resolution central weather station data allowed detailed mapping of temperature variations across districts. This cross-scale projection model is especially valuable for highly developed areas, enabling more accurate building energy consumption estimates. Machine learning methods proved effective in identifying heat-contributing factors and quantifying their impacts across seasons and spatial scales. Empirical results indicate that a 10% increase in blue-green space can lower urban air temperatures by 0.22–0.31°C, while a 10% increase in building density or impervious surfaces raises temperatures by 0.27–0.37°C. Calibration with Climate Observation Data Inquire System (CODiS) data significantly improved projection accuracy, with XGBoost error rates under 10% and R² ranging from 0.73 to 0.77, the average RMSE is 1.2–1.5°C and MAE is 1.0–1.2°C, outperforming traditional linear methods. These findings provide scientific support for urban thermal management and policy-making. Future integration of more urban morphology and long-term climate data will further optimize models and support sustainable urban development in Taiwan and similar climates.

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