2026-03-12
【學術亮點-頂級期刊論文】利用極端梯度提升遷移學習增強資料稀缺情境下的路面溫度預測
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Intelligent Service: Large-scale Agricultural AI Models
【Department of Civil Engineering / Ming-Der Yang / Tenured Distinguished Professor】
智慧服務：可大規模擴展之農業AI 模型【土木工程學系楊明德終身特聘教授】
上架日期2026-03-02
【Department of Civil Engineering / Ming-Der Yang / Tenured Distinguished Professor】
智慧服務：可大規模擴展之農業AI 模型【土木工程學系楊明德終身特聘教授】
| 論文篇名 | 英文：Enhancing pavement temperature prediction using eXtreme gradient boosting transfer learning for data-scarce scenarios 中文：利用極端梯度提升遷移學習增強資料稀缺情境下的路面溫度預測 |
| 期刊名稱 | Engineering Applications of Artificial Intelligence (指標清單期刊) |
| 發表年份, 卷數, 起迄頁數 | 2026,172, no.114316 |
| 作者 | Yared Bitew Kebede, Ming-Der Yang(楊明德)*, Henok Desalegn Shikur |
| DOI | 10.1016/j.engappai.2026.114316 |
| 中文摘要 | 準確預測路面溫度分佈對於主動維護和有效的基礎設施管理至關重要。然而，傳統的機器學習模型通常需要龐大的、特定位置的資料集，而收集這些資料成本高且耗時，限制了它們在實際場景中的應用。為了應對這項挑戰，我們提出了一種基於極限梯度提升（XGB）的新型遷移學習框架（XGB-T），旨在提高資料匱乏條件下的預測精度。此方法首先使用來自台灣中部地區的綜合資料集對模型進行預訓練，以捕捉路面溫度分佈；然後使用來自台灣南部地理位置不同的目標地點的最小資料集進行微調。這種兩階段過程使得模型能夠以最少的額外資料將學習到的知識遷移到新的領域。我們對 XGB-T 的表現進行了嚴格的評估，將其與從頭開始訓練的標準極限梯度提升（XGB）模型以及幾種其他的遷移學習模型（包括輕量級梯度提昇機、隨機森林、自適應提升、長短期記憶網絡和門控循環單元）進行了比較。結果表明，XGB-T 模型顯著優於基準 XGB 模型，在測試階段所有深度下，平均絕對誤差降低了 58.96%，平均絕對百分比誤差降低了 56.77%，決定係數提高了 4.08%（從 0.942 提高到 0.987）。這些發現驗證了 XGB-T 模型作為一種高效且資源節約的路面溫度預測解決方案，能夠在各種數據有限的環境中保持高預測精度，同時顯著降低數據採集的負擔和成本。這個穩健的框架有助於及時決策，改善基礎設施維護策略，並最終提高道路安全。 |
| 英文摘要 | Accurate prediction of pavement temperature profiles is crucial for proactive maintenance and effective infrastructure management. However, traditional machine learning models often require large, location-specific datasets that are costly and time-consuming to collect, limiting their practical application in real-world scenarios. To address this challenge, we propose a novel transfer learning framework based on eXtreme Gradient Boosting (XGB-T), designed to enhance prediction accuracy under data-scarce conditions. The approach involves pre-training the model on a comprehensive dataset from central Taiwan to capture pavement temperature profile, followed by fine-tuning using a minimal dataset from a geographically distinct target site in southern Taiwan. This two-stage process allows the model to transfer learned knowledge to a new domain with minimal additional data. The performance of XGB-T was rigorously evaluated against a standard eXtreme Gradient Boosting (XGB) model trained from scratch and several alternative transfers learning models, including Light Gradient Boosting Machine, Random Forest, Adaptive Boosting, Long Short-Term Memory, and Gated Recurrent Unit. Results show that XGB-T significantly outperforms the baseline XGB model, achieving a 58.96% reduction in Mean Absolute Error, a 56.77% decrease in Mean Absolute Percentage Error, and a 4.08% improvement in the coefficient of determination (from 0.942 to 0.987), averaged across all depths in the testing phase. These findings validate XGB-T as a highly effective and resource-efficient solution for pavement temperature forecasting, significantly reducing data collection burden and cost while maintaining high predictive accuracy in diverse, data-limited environments. This robust framework supports timely decision-making, improves infrastructure maintenance strategies and ultimately enhances road safety. |
| 發表成果與AI計畫研究主題相關性 | 目前發展的AI 模式以道路鋪面為溫度預測對象，可以應用在農地土壤分層溫度之預測。 |
