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：Estimating the Daily Average Concentration Variations of PCDD/Fs in Taiwan Using a Novel Geo-AI Based Ensemble Mixed Spatial Model 中文：透過全新的地理人工智慧學習(Geo-AI)集成式混合空間模型以探討台灣PCDD/Fs每日平均濃度變化情形 |
| 期刊名稱 | Journal of Hazardous Materials |
| 發表年份,卷數,起迄頁數 | 2023, 458:131859 |
| 作者 | Hsu, Chin-Yu; Lin, Tien-Wei; Babaan, Jennieveive B.; Asri, Aji Kusumaning; Wong, Pei-Yi; Chi, Kai-Hsien; Ngo, Tuan Hung; Yang, Yu-Hsuan; Pan, Wen-Chi; Wu, Chih-Da(吳治達)* |
| DOI | 10.1016/j.jhazmat.2023.131859 |
| 中文摘要 | 有鑑於PCDD/Fs對人類健康有害，因此需要進行大量的現場研究。本研究為首次使用以集成混合空間模型(EMSM)為基礎的新型的地理空間人工智慧（Geo-AI）模型，該模型集成了多個機器學習算法和使用SHAP值選擇的地理預測變數以預測台灣本島PCDD/Fs濃度的時空趨勢。該模型使用2006年至2016年的每日PCDD/F的I-TEQ當量資料來建立，並使用外部數據驗證了模型的可靠性。我們利用了Geo-AI，整合克利金法、五種機器學習和集成方法（這五種模型的組合）來開發EMSM。EMSM用於估計PCDD/F I-TEQ水平的長期時空變化，考量研究期間現場量測、氣象、地理、社會因數以及季節性影響。研究結果表明，EMSM模型優於所有其他模型，解釋能力提高至87%。根據結果顯示，PCDD/F濃度的在於時間上的波動可能是天氣狀況的結果，而地理變異可能是由於城市化及工業化。這些結果提供了支持污染控制措施和流行病學研究的準確估算。 |
| 英文摘要 | It is generally established that PCDD/Fs is harmful to human health and therefore extensive field research is necessary. This study is the first to use a novel geospatial-artificial intelligence (Geo-AI) based ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and geographic predictor variables selected using SHapley Additive exPlanations (SHAP) values to predict spatial-temporal fluctuations in PCDD/Fs concentrations across the entire island of Taiwan. Daily PCDD/F I-TEQ levels from 2006 to 2016 were used for model construction, while external data was used for validating model dependability. We utilized Geo-AI, incorporating kriging, five machine learning, and ensemble methods (combinations of the aforementioned five models) to develop EMSMs. The EMSMs were used to estimate long-term spatiotemporal variations in PCDD/F I-TEQ levels, considering in-situ measurements, meteorological factors, geospatial predictors, social and seasonal influences over a 10-year period. The findings demonstrated that the EMSM was superior to all other models, with an increase in explanatory power reaching 87 %. The results of spatial-temporal resolution show that the temporal fluctuation of PCDD/F concentrations can be a result of weather circumstances, while geographical variance can be the result of urbanization and industrialization. These results provide accurate estimates that support pollution control measures and epidemiological studies. |
| 發表成果與本中心研究主題相關性 | 本研究成果可用於評估農業地區戴奧辛污染狀況做為未來永續農業發展之基礎。 |
