2024-05-23
【學術亮點】於有限氣象變數條件下使用人工智慧算法進行每日參考蒸散發量的估計和短期預報
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【學術亮點】於有限氣象變數條件下使用人工智慧算法進行每日參考蒸散發量的估計和短期預報
Intelligent Detection: Development of expert diagnosis system for crop cultivation and management【Department of Agronomy / Bo-Jein Kuo / Professor】
智慧檢測：作物栽培管理專家診斷系統開發【農藝系郭寶錚教授】
上架日期：2024/3/22
Intelligent Detection: Development of expert diagnosis system for crop cultivation and management【Department of Agronomy / Bo-Jein Kuo / Professor】
智慧檢測：作物栽培管理專家診斷系統開發【農藝系郭寶錚教授】
| 論文篇名 | 英文：Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables 中文：於有限氣象變數條件下使用人工智慧算法進行每日參考蒸散發量的估計和短期預報 |
| 期刊名稱 | Agriculture |
| 發表年份, 卷數, 起迄頁數 | 2023, 14(4), 510 |
| 作者 | Shih-Lun Fang,Yi-Shan Lin,Sheng-Chih Chang,Yi-Lung Chang,Bing-Yun Tsai and Bo-Jein Kuo*(郭寶錚) |
| DOI | 10.3390/agriculture14040510 |
| 中文摘要 | 參考蒸散發量（ reference evapotranspiration, ET0）的資訊對於灌溉規劃和水資源管理至關重要。儘管 Penman-Monteith方程式被廣泛認可用於計算ET0，但其需要眾多氣象參數的特性限制了其應用性。本研究利用台灣四個地理區18個測站長達28年的氣象資料，評估了人工智慧（artificial intelligence, AI）模型以有限氣象變數作為模型輸入估計ET0的效能，並與傳統方法進行比較。此外，AI模型還被用於以有限氣象變數進行ET0的短期預報。研究結果顯示，AI模型在ET0估算方面的表現優於其對應的傳統方法。其中以使用溫度、太陽輻射和相對濕度作為輸入變數的人工神經網絡表現最佳，該模型的相關係數達0.992到0.998，平均絕對誤差（mean absolute error, MAE）為0.07到0.16 mm/天，均方根誤差（root mean square error, RMSE）為0.12到0.25 mm/天。對於ET0短期預報，使用溫度、太陽輻射和相對濕度作為輸入變數的長短期記憶模型是最佳結構，該模型預報四天後的ET0之相關係數達0.608到0.756，MAE為1.05到1.28 mm/天，RMSE為1.35到1.62 mm/天。在超過一年的測時期間，該結構的百分比誤差於多數氣象站在±5％以內，顯現出本研究所提出的模型具備一定程度的準確性。最後，本研究所提出的ET0估計模型和預報模型皆屬於區域模型且僅需少數氣象變數作為輸入，使其在實際應用中具有極大的優勢。 |
| 英文摘要 | The reference evapotranspiration (ET0) information is crucial for irrigation planning and water resource management. While the Penman-Monteith (PM) equation is widely recognized for ET0 calculation, its reliance on numerous meteorological parameters constrains its practical application. This study used 28 years of meteorological data from 18 stations in four geographic regions of Taiwan to evaluate the effectiveness of an artificial intelligence (AI) model for estimating PM-calculated ET0 using limited meteorological variables as input and compared it with traditional methods. The AI models were also employed for short-term ET0 forecasting with limited meteorological variables. The findings suggested that AI models performed better than their counterpart methods for ET0 estimation. The artificial neural network using temperature, solar radiation, and relative humidity as input variables performed best, with the correlation coefficient (r) ranging from 0.992 to 0.998, mean absolute error (MAE) ranging from 0.07 to 0.16 mm/day, and root mean square error (RMSE) ranging from 0.12 to 0.25 mm/day. For short-term ET0 forecasting, the long short-term memory model using temperature, solar radiation, and relative humidity as input variables was the best structure to forecast four-day-ahead ET0, with the r ranging from 0.608 to 0.756, MAE ranging from 1.05 to 1.28 mm/day, and RMSE ranging from 1.35 to 1.62 mm/day. The percentage error of this structure was within ± 5% for most meteorological stations over the one-year test period, underscoring the potential of the proposed models to deliver daily ET0 forecasts with acceptable accuracy. Finally, the proposed estimating and forecasting models were developed in regional and variable-limited scenarios, making them highly advantageous for practical applications. |
| 發表成果與AI計畫研究主題相關性 | 參考蒸散發量的資訊對於灌溉規劃和水資源管理至關重要，本研究利用台灣四個地理區18個測站長達28年的氣象資料，評估人工智慧（artificial intelligence, AI）模型以有限氣象變數作為模型輸入估計參考蒸散發量的效能，並與傳統方法進行比較在超過一年的測時期間，該結構的百分比誤差於多數氣象站在±5％以內，顯現出本研究所提出的模型具備一定程度的準確性。最後，本研究所提出的參考蒸散發量估計模型和預報模型皆屬於區域模型且僅需少數氣象變數作為輸入，使其在實際應用中具有極大的優勢。 |
