## 2 Literature Review on Enhancing Hydroelectric Power Plant Efficiency  
With the advancement of technology, machine learning perceives and analyzes data to solve real-world problems. Artificial neural networks, on the other hand, can learn from data to recognize patterns and partition the data into layers of abstraction [4]. Hydrological issues can be addressed through mathematical models that are computed with the assistance of computers. Artificial neural networks are employed to generate solutions for intricate problems related to hydrological phenomena [5]. Optimizing energy production and planning processes in a hydropower plant is a significant and complex challenge, considering technological, economic, physical, and environmental factors. In this study, it is shown how to produce energy most efficiently in a HEPP. The two most frequently mentioned approaches for enhancing power efficiency are managing reservoir operations and regulating water flow. This process involves various uncertainties, including volatile demand, water availability, weather conditions, and financial considerations [6]. The amount of energy produced is closely linked to water pressure and the power generated at the turbine shaft. This mechanical power can be calculated using the \(P\) Equation (1) [6]. \[P=\eta t\,\rho w\,g\,Q\,h \tag{1}\]  
where \(\eta t=\) hydraulic efficiency of the turbine, \(\rho w=\) water intensity, \(g=\) acceleration due to gravity, \(Q=\) flow rate of water acting on the turbine, \(h=\) height of water acting on the turbine. Equation (1) shows that the power produced at the generator shaft is directly reliant on the flow of water. A hydropower plant experiences variability in water flow throughout the year. Effectively utilizing this incoming water is crucial for optimal operation. [PERSON] et al. employed machine learning models to predict energy production at a hydroelectric power plant, achieving the highest accuracy with the adaptive neuro-fuzzy inference system (ANFIS) method [7]. [PERSON], [PERSON], and [PERSON] used an artificial neural network model to predict energy inputs in hydroelectric power plants [8]. [PERSON] et al. used an artificial neural network to estimate electricity production and water consumption in a hydroelectric power plant [9]. [PERSON] et al. utilized artificial neural networks (ANNs) to forecast hydropower generation, with model parameters optimized through a grid search algorithm [10]. The resulting model proved to be a dependable tool for energy management. [PERSON] et al. introduced an ANN model for forecasting power one day in advance [11]. [PERSON] et al. developed LSTM, convolutional neural network (CNNE), and recurrent neural network models (RNN) to predict power energy [12]. [PERSON] et al. presented a study at the Manwan Hydroelectric Power Plant in Yunnan Province, China, where the application of a genetic algorithm integrated with dynamic programming increased flexibility in output allocation, reducing leaks by 79% and expanding high-efficiency zones by 43% [13]. [PERSON] et al. developed the butterfly optimization algorithm (BOA), a newly developed meta-heuristic method that is widely used in solving various optimization problems to realize the optimal operation of the reservoir [14]. [PERSON] et al. proposed a new approach called the state-of-the-art algorithm stochastic dual dynamic programming STRO to solve the mid-term medium-term reservoir management problem [15]. [PERSON] et al. established the coordination model of electricity generation and ecological flow using the multi-objective genetic algorithms (MOGAs) method for the operation of the cascade hydropower system [16]. [PERSON] et al. presented a hybrid deep learning model to predict hydropower production [17]. [PERSON] et al. proposed a multi-objective optimization operation model to optimize the seasonal flood-limited water levels (FLWLs) of the reservoir in order to maximize hydropower benefits and reduce flood risk [18]. Inorder to benefit from the advantages of hydropower, [PERSON] et al.