臺灣地區，由於山地地形關係平均坡度陡峻，豪雨季節容易山洪暴漲氾濫成災，乾旱季節則見旱象危機無水可用，因此興建水庫和構築堰壩，成為政府重要水資源政策，除了蓄水及兼具河川整治與防洪減災功能外，更可創造發電、觀光附加價值。
而在臺灣人口密度高居世界各國第2位情況下(國家人口數達1,000萬以上比較)，已構築大小不等的水庫及蓄水設施100餘座，各式堰壩更達200座以上。有鑑於國外曾發生多起水庫大壩潰堤，造成重大人員傷亡之慘劇，針對水壩安全性除了要求工程規劃、設計及施工之技術品質因素外，營運啟用的安全維護與管理更是永續水資源之不二法門，因此作好壩體變位量之監控，直接影響水庫設施運作之安全。
本研究蒐集翡翠水庫大壩歷年來監測資料，彙整分析可能影響壩體變位量之初步因子選項，利用統計軟體SPSS對初步因子與輸出變數(變位)進行相關性分析，客觀挑選出影響壩體變位量之重要因子作為研究模型的輸入參數，並應用不同的人工智慧理論，進行案例資料庫的學習訓練，再以各種推論模式進行測試，得到壩體變位量之預測成果值。
為驗證各種人工智慧推論模式之預測準確性，本研究模擬壩體變位曲線，展現各種模型預測成果比較，並分別以線性相關係數(Linear Correlation Coefficient， R)、均方根誤差 (Root Mean Square Error， RMSE)、平均絕對誤差(Mean Absolute Error， MAE)、平均絕對百分比誤差(Mean Absolute Percent Error， MAPE)進行預測準確性之誤差衡量，最後再以綜合性指數(Synthesis Index, SI)做為整體成果評估標準而得到驗證結論，結果以「自調適時間函數之演化式最小平方差支持向量機推論模式」(ELSIMT)最佳。

In Taiwan, the average gradient is steep owing to mountainous terrain; therefore, the occurrence of flash floods in the monsoon and drought crises in the dry season is common. Therefore, the construction of reservoirs and barrages has become an important facet of government water policy. In addition to functions like water storage, river regulation, and flood control, reservoirs and barrages provide additional value in the form of power generation and tourist attractions.
As Taiwan has the second-highest population density in the world (among countries with a population of 10 million or more), there are approximately 100 reservoirs and water storage facilities of different sizes and over 200 barrages of several types. Globally, several dam burst incidents have occurred, causing heavy casualties. Therefore, for dam safety, in addition to the requirements of factors such as project planning and design and technical quality of construction, security maintenance and management during operation is extremely essential for the sustainable utilization of water resources. Therefore, monitoring dam displacement directly influences the operational safety of the reservoir facilities.
This study involved collecting monitoring data of the Emerald dam over several years and analyzing preliminary factors that might influence the dam displacement. Thereafter, correlation analysis between preliminary factors and the output variable (displacement) was conducted using a statistical software (IBM SPSS) to select significant variables that influence the dam displacement as input parameters of research models. After the application of different artificial intelligence theories, learning and training in the case database, and testing by a variety of inference models, the predictions of the dam displacement were obtained.
To verify the prediction accuracy of various artificial intelligence inference models, this study simulated dam displacement curves and presented the comparison of prediction results among different models. The prediction accuracy was measured by Linear Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percent Error (MAPE). Finally, the Synthesis Index (SI) was used as the evaluation criteria of the overall results for verification. Adaptive Time-dependent Evolutionary Least Squares Support Vector Machine Inference Model (ELSIMT) was determined to be the best model.

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