###### Abstract
The goal of this project is to develop a class-based extreme learning machine that can determine the optimal operation rule for a hydropower reservoir. Through a k-means clustering algorithm, the cluster analysis is performed to split the influence factors into several sub-regions. The extreme learning machine is then optimized by particle swarm analysis to identify the complex relationship between the cluster's input and output. [PERSON] et al. developed [15] a class-based extreme learning machine that can determine the optimal operation rule for a hydropower reservoir. Through a k-means clustering algorithm, the cluster analysis is performed to split the influence factors into several subregions. The extreme learning machine is then optimized by particle swarm analysis to identify the complex relationship between the cluster's input and output. [PERSON] et al. proposed [16] an approach that can improve the quality of the monitoring data collected from hydropower units by implementing a clustering algorithm. This approach can be used to solve various problems related to the condition monitoring. A standard system to classify the huge amount of information that is collected and stored has been proposed by researchers in the study performed [17]. The system can meet the needs of the DEMA and provide them with the necessary services. [PERSON] et al. [18] used three approaches, Local Outlier Factor as a density-based method, Feature Bagging for Outlier Detection as an ensemble method, and Subspace Outlier Degree, to analyze the anomalous data collected from a hydropower plant and compare their performance. The outliers were then verified by the expert utilizing a feature selection process and a decision tree to identify the critical variables that could be associated with the anomalies. [PERSON] et al. presented [19] a procedure that uses Knowledge Discovery to analyze a data set and extract structured information related to a hydroelectric power plant. This method can be utilized to train systems focused on identifying faults. [PERSON] et al. proposed [20] a decision tree-based clustering scheme that can be used to determine the various operating regimes in hydropower plants. The method uses k-means++ clustering to classify the data. The decision tree is then constructed using the group labels and other features. The decision tree is then analyzed and pruned according to the classification accuracy and complexity requirements. The reference [21] introduced the data mining concept integrated into a SCADA system to help the hydropower plant's operators make informed decisions. The data collected by the data mining process can determine the typical loading profiles of each generation unit. [PERSON] and [PERSON] presented [22] a study on the energy generation forecasting of a hydroelectric plant based on Machine Learning and a hybrid Genetic Grey Wolf Optimizer-based Convolutional Neural Network/Recurrent Neural Network-Long Short-Term Memory regression approach. The findings can help improve the efficiency of resource management and energy generation. Two aspects draw attention concerning the current applications of Knowledge Discovery (KD) in hydropower plants:  
1. The vast majority of applications aim for a single task implemented at the level of data analysis regarding the energy production relationship between upstream and downstream hydro plants, energy production forecasting, identifying the operating regimes, improving the data quality, outliers' detection, identifying faults, or determining the typical operating profiles. 2. The analysis time horizon corresponds with a day, season, or year.