近幾年來，由於溫室效應的影響造成全球氣候環境劇烈的改變，專家們開始大量地對於二氧化碳排放量進行研究，而二氧化碳排放量也就是影響溫室效應最大的元凶。因此，本研究對於二氧化碳排放量提出了一個混合型預測演算法模型，其主要是以萬用演算法為基礎之直覺模糊類神經網路演算法。本研究所使用的萬用演算法為差分進化演算法，藉由這個萬用演算法來尋找直覺模糊神經網路的最佳參數。首先將資料集進行資料前處理，將資料去除離異值，接著對資料集進行正規化，減少資料差異性太大或離異值的影響，令資料較容易被直覺模糊類神經網路演算法使用，接著將資料集分為訓練資料集以及測試資料集，透過差分進化演算法調整直覺模糊類神經網路的參數以建置訓練預測模型，再將測試資料集放入預測模型進行預測。
為了驗證本研究所提出之整合預測模型是否準確，本研究使用了k-fold，將資料集分成十等分，以驗證在十個等分的資料集都能有好的預測結果。實驗結果證明，以應用差分進化演算法為基礎之直覺模糊神經網路，相對於傳統預測演算法來說，有較好的預測結果。
另外，本研究亦蒐集四個影響二氧化碳排放量的指標，分別是人口、國內生產總值、能源強度及碳強度，作為針對台灣二氧化碳排放量的預測輸入屬性進行預測。實驗結果證明，以應用差分進化演算法為基礎之直覺模糊神經網路能夠對於台灣二氧化碳排放量進行預測，並有較好的預測結果。

In recent years, global climate environment issue becomes a very hot topic. Many researches have studied the carbon dioxide emission which is also the key issue in the greenhouse effect. This study proposes a hybrid forecasting model to predict the carbon dioxide emission. The proposed forecasting model is developed using a metaheuristic-based intuitionistic fuzzy neural network. This study applies a differential evolution algorithm to find the best parameters for the intuitionistic fuzzy neural network. In order to obtain a better result, this study also conducts a data preprocessing. It aims to remove the outliers. In addition, the data is also normalized to decrease the gap differences between attributes.
This study applies the proposed algorithm on the carbon dioxide dataset in Taiwan. The dataset is divided into two sets, training and testing data. The training algorithm is used to train the proposed algorithm and build the forecasting model. After training, the proposed algorithm is applied to predict the testing dataset. For validation, this study conducts a K-fold cross validation. The experimental results indicate that the differential evolution algorithm-based intuitionistic fuzzy neural network has better performance than other traditional forecasting algorithms.
In addition, this study applies four indicators which influence carbon dioxide emission in Taiwan including population, GDP, energy intensity, and carbon intensity to be inputs to carbon dioxide emission forecasting. The experimental results indicate that the differential evolution algorithm-based intuitionistic fuzzy neural network has better performance than other traditional forecasting algorithms.

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