[[alternative]]Integrating AI with Expert Knowledge to Build Intelligent Reservoir Operation System

[[abstract]]面對台灣地區水資源時空分佈不均及日益不足等問題，如何在安全條件下進行水庫操作使其儘可能滿足各標的。以善用水資源、維持環境永續性是當前首要課題。本研究以新穎的人工智慧相關理論，並結合現行規線操作的專家知識提出智慧型水庫操作策略，以石門水庫過去36年之水文狀況為例，進行實務模擬測試；首先利用遺傳演算法(GA)尋求歷史流量之水庫最佳放水量歷程，以茲作為調適性網路模糊推論系統(ANFIS)之訓練樣本與標的。為增加系統操作規則庫之完整性與合法性，乃研議水庫操作規線與模糊規則庫之問的轉換方式與機制，將操作規線所代表之蓄放標準轉換為規則(if-then)形式，建構出模糊規則知識庫，成功的將水庫傳統的操作策略與智慧型操作模式進行結合，藉由加入傳統操作方式的專家知識使系統更具『智慧』地處理資料與判斷資訊，進而有效地控制水庫水位與其放流量，提供水庫管理單位於蓄水利用運轉時有所參考及依據，測試結果顯示本研究所發展的模式較傳統規線操作方式在各項檢測指標上皆有大幅的改善，亦印證了模式的合理性與適切性。[[abstract]]Resulting from the continuous increase in water demand and uneven water distribution both on time and space, the efforts of pursuing integrated optimal water resource management become critical. In this study, we propose a novel intelligent control methodology that includes the genetic algorithm (GA), fuzzy rule base (EBB), and the adaptive network-based fuzzy inference system (ANFIS) to enhance the efficiency of reservoir operation. The Shihmen reservoir in north Taiwan is used as a case study, and its last thirty-six years hydrological data are used to train and/or verify the model's performance. GA and FRB are used to extract the knowledge based on the historical inflow data with a design objective function and the traditional rule curve operating strategy, respectively. The ANFIS is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. The practicability and effectiveness of the proposed approach is tested on the operation of the Shihmen reservoir. The results show that the ANFIS models built on different...[[journaltype]]國內[[booktype]]紙本[[countrycodes]]TW