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論文名稱
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結合溫室氣體排放、綠蔽度衛星影像與土地利用資料的環境溫度機器學習預測模型開發
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Title
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Development of an Ambient Temperature Prediction Model Using Machine Learning by Integrating Greenhouse Gas Emissions, Vegetation Index Satellite Images, and Land Use Data
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作者
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張皓庭、陳映融、柳婉郁、吳治達
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Author
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Hao-Ting Chang, Yinq-Rong Chern, Wan-Yu Liu, Chih-Da Wu
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中文摘要
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本研究綜合考量了溫室氣體、環境和人為活動相關變數，以利用大數據與五種機器學習演算，包含：隨機森林 (RF)、梯度提升 (GBR)、輕量梯度提升 (LGBMR)、類別提升 (CBR) 和極限梯度提升 (XGBoost)來建立兩種溫室氣體CO2和CH4推估環境溫度的模型，其中LGBMR模型在CO2方面表現最佳，而XGBR模型在CH4方面效果較好。CO2和CH4推估環境溫度模型的表現，R2值分別為0.993和0.999。SHAP值的分析確認了溫室氣體濃度、濕度、風速等因素對預測的關鍵影響。本研究成果為溫室氣體減排策略提供了新的評估方法，並為全球氣候變化研究與政策制定提供了重要參考，凸顯了跨學科合作的重要性。
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Abstract
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This study integrated greenhouse gases, environmental, and anthropogenic variables, utilizing big data and five machine learning algorithms, including Random Forest (RF), Gradient Boosting (GBR), Light Gradient Boosting Machine Regressor (LGBMR), CatBoost Regressor (CBR), and eXtreme Gradient Boosting (XGBoost), to establish models for estimating ambient temperatures based on two greenhouse gases, CO2 and CH4. The LGBMR model performed best for CO2, while the XGBR model showed better performance for CH4. The R2 values for the CO2 and CH4 estimation models were 0.993 and 0.999, respectively. Analysis of SHAP values confirmed the significant influence of greenhouse gas concentration, humidity, wind speed, and other factors on predictions. The findings of this study offer new evaluation methods for greenhouse gas emission reduction strategies and provide crucial insights for global climate change research and policy-making, highlighting the importance of interdisciplinary collaboration.
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關鍵字
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環境溫度、二氧化碳、甲烷、機器學習預測模型、機器學習演算法
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Keywords
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Ambient Temperature, Carbon Dioxide, Methane, Machine Learning Predictive Model, Machine Learning Algorithms
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