Investment estimation is an essential part of hydropower projects. This paper proposes a learning rate control-enabled deep learning neural network model that can be optimized for different data sizes, especially when small. Then, a DNN model with learning rate optimization is constructed based on the existing hydropower project data in China; finally, the practicality and reliability of the learning rate control enabled example calculations to verify the DNN model. According to the results, the learning rate control-enabled DNN model accurately predicts outcomes. Therefore, it can achieve accurate, fast, and adequate investment estimation for large-scale and middle-scale hydropower projects.
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