生態農業：氣候變遷下森林碳匯與生態經濟評估【國立成功大學測量及空間資訊學系/吳治達教授】
| 論文篇名 | 英文：A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan 中文：基於機器學習的集成模型，用於估算台灣氮氧化物濃度的日間變化 |
| 期刊名稱 | Science of the Total Environment |
| 發表年份,卷數,起迄頁數 | 2024, 916: 170209 |
| 作者 | Asri, Aji Kusumaning; Lee, Hsiao-Yun; Chen, Yu-Ling; Wong, Pei-Yi; Hsu, Chin-Yu; Chen, Pau-Chung; Lung, Shih-Chun Candice; Chen, Yu-Cheng; Wu, Chih-Da(吳治達)* |
| DOI | 10.1016/j.scitotenv.2024.170209 |
| 中文摘要 | 空氣污染與人類活動模式密不可分，氮氧化物（NOx）尤其如此，因為它既自然存在也由人為因素產生。考慮日間變化來評估暴露是一個挑戰，尚未得到廣泛研究。本研究利用27年的數據，試圖估算台灣地區NOx的日間變化。我們開發了一種基於機器學習的集成模型，整合了混合克里金-土地利用回歸（kriging-LUR）、機器學習和集成學習方法。混合克里金-土地利用回歸用於選擇最具影響力的預測因子，並應用機器學習算法以提升模型性能。經過調整的三種最佳機器學習算法被用於開發集成學習模型，以提高模型表現。我們的集成模型對白天、夜間和全天NOx的預測分別達到0.93、0.98和0.94的高解釋力（Adj-R²），這些解釋力較最初僅使用混合克里金-土地利用回歸的模型有所提升。此外，結果顯示NOx的時間變化，白天濃度高於夜間。就空間變化而言，台灣北部和西部的NOx濃度最高。模型評估確認了模型的可靠性。本研究可作為區域規劃的參考，支持環境和人類健康的排放控制。 |
| 英文摘要 | Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health. |
| 發表成果與本中心研究主題相關性 | 本研究探討之主題著眼在地理人工智慧於環境汙染與永續環境管理之應用，與本中心高度相關。 |
