中文摘要
長期以來，全球氣候變遷對農業生產產生了深遠影響。不幸的是，最近的冠狀病毒大流行和俄烏戰爭進一步加劇了農業產出的短缺。因此，各國政府不可能根據過去的經驗制定好的農業政策。然而，一個好的預測工具可以幫助政府將歷史數據轉化為有用的資訊，從而簡化農業決策。
基於上述原因，我們提出了兩種混合機器學習預測模型。混合模型I是一個由支援向量廻歸和基因演算法用於預測台灣地區水稻產量的組成。在此模型中，我們利用台灣中央氣象局的氣象數據和地理區域因素來預測2003年至2019年台灣水稻產量。實驗結果顯示：(1) 影響水稻產量的氣候因素包括總日照時數、降雨天數和溫度；(2) 混合模型I用於不同的地理區域，皆有很高的預測精度；(3) 混合模型Ⅰ對水稻產量的預測更可靠、更穩定。
混合模型II是蜜蜂最佳化演算法和支持向量廻歸的混合模型，用於減輕高度波動數據的影響，從而提高預測的準確性。在此模型中，我們使用從台灣行政院農業委員會統計年鑑中收集的年度農業總產值（台灣年度農業總產值，包括農產品、畜禽產品、水產品和林產品）來驗證混合模型 II 的效能。結果證實，所提出的模型比其他模型更準確地預測長期農業產值。因此，混合模型II是一個具有高預測精度的穩健模型，可以幫助農業從業者制定提高農業產值的政策。

Abstract
For a long time, climate change around the world has profoundly impacted agriculture output. Unfortunately, the recent coronavirus pandemic and Russian-Ukrainian War have further aggravated the shortage of agricultural output. Therefore, governments of different countries cannot make good agricultural policies according to their past experiences. However, a good forecasting tool may help the government to convert historical data into useful information to ease decision-making in agriculture.
Therefore, we propose two hybrid machine-learning prediction models. The hybrid model I consists of a Support Vector Regression model and a Genetic Algorithm for predicting rice production in Taiwan. In this model, we used meteorological data from the Central Weather Bureau of Taiwan and the geographic region factors to predict rice production in Taiwan from 2003 to 2019. The experimental results showed that: (1) the climatic factors affecting rice yield included total sunshine hours, rainfall days, and temperature; (2) the hybrid model I had high prediction accuracy for different geographical regions; and (3) rice yield prediction using the hybrid model I were more reliable and stable.
The hybrid model II is a hybrid model of Honey Bees Optimization algorithm and Support Vector Regression, which is used to mitigate the effect of highly volatile data and thus improve the accuracy of prediction. In this model, we validated the performance of the hybrid model II by using the annual total agricultural output values (annual total agricultural output values in Taiwan including agricultural products, livestock and poultry products, aquatic products, and forest products) collected from the official agricultural statistical yearbook of Taiwan Council of Agriculture. The results confirmed that the proposed model predicted long-term agricultural output more accurately than other models. Thus, the hybrid model II is a robust model with high prediction accuracy that can assist agricultural practitioners in making policies for improving agricultural output prediction.

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