水資源為工業製程中重要的生產要素之一，本研究嘗試從水資源的消耗量推估經濟發展，應用隨機森林演算法，以某產業園區的配水量變化預測台灣股票指數趨勢。股票指數以與園區產業最相關的電子類報酬指數為主，並細分8個產業類別分項進行預測，包含半導體類、電腦及週邊設備類、光電類、通信網路類、電子零組件類、電子通路類、資訊服務類與其他電子類，模型亦延伸預測整體市場變化（發行量加權股價指數）。
以配水量作為領先指標，透過投入60日的配水量數據以預測單日股票指數。研究結果發現，配水量與股票指數間的遞延關係多落在4-5個月或7-8個月，在預測效果上，對於電子類報酬指數與整體市場皆有不錯成效，模型的決定係數皆大於0.8，驗證集與測試集之MAPE皆小於10 %，顯見該產業園區配水量對於電子類產業及整體市場具備預測能力。模型成效最佳者為資訊服務類，測試集MAPE達1.44 %。與園區配水量最直接相關的產業屬半導體業與光電業，光電業卻因相關係數接近於零，顯見兩者相關性低，不適合建立預測模型。

In this study, the Taiwan stock index is predicted from the water consumption in an industrial park by the random forest algorithm. As water consumption may not reflect the stock change instantly, the correlation between the stock index and water consumption is used to determine the number of months by which the stock index may lag behind. The data was divided into training, validation, and testing sets; the random forest model was trained using a 60-day dataset of water consumption to predict the daily stock index.
The research findings reveal that there is a deferred relationship between water consumption and the stock index, mostly falling within 4-5 months or 7-8 months. The model demonstrates good performance across various market types, with Rd2 being greater than 0.8. The mean absolute percentage error (MAPE) for both validation and testing sets is below 10 %. This indicates that water consumption in the industrial park can effectively predict the Electronics Index and the overall market (TAIEX Total Return Index).

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