Water Level Prediction Using BP Neural Networks

In this paper, we explore water level prediction using Backpropagation (BP) Neural Networks. This approach leverages neural network technology to forecast water level variations, significantly improving prediction accuracy and computational efficiency. To implement this methodology, we need to collect historical water level data and feed it into the neural network for analysis and forecasting. We will discuss optimizing the neural network's architecture (including hidden layer configuration and neuron count) and parameters (such as learning rate and activation functions) to achieve optimal prediction results. The implementation typically involves data preprocessing, network initialization using functions like newff in MATLAB, and iterative training through gradient descent algorithms. This BP neural network-based water level prediction method finds applications across multiple domains including hydrology, meteorology, and environmental engineering, demonstrating broad practical prospects.