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This paper introduces the composition, classification protection and revitalization principles, methods and modes of ancient architectural heritage in Fuzhou, and points out that The Technical Guidelines for the Protection, Repair and Renovation of Historic Buildings in Fuzhou is a comprehensive reference to The Requirements for the Preparation of Design Documents for Cultural Relics Protection Projects (for Trial Implementation). Based on the perspective of heritage, this paper proposes that the unclear connotation and boundary of architectural heritage types have led to the over-protection of some types of Fuzhou ancient buildings, and attempts to achieve the purpose of scientific classification and protection of Fuzhou ancient buildings through the adjustment of the guidelines, and constructing the protection mode and protection framework, which provides a theoretical basis for the construction of urban and rural historical and cultural protection and inheritance system. [ABSTRACT FROM AUTHOR]

The prediction of urban river water level is of great significance to the risk management of urban waterlogging. Traditional numerical simulation models have low computational efficiency and are unable to perform real-time calculations. In response to the above issues, this paper proposes a channel water level prediction model based on Gaussian function improved BP neural network, which solves the problems of low prediction accuracy and slow convergence speed in error flat areas of the BP neural network model. This method utilizes the Gaussian function to improve the gradient descent algorithm of the BP neural network, sets different learning rates for different weights and threshold values of the model, and optimizes each parameter accordingly, which can effectively accelerate the training efficiency of the BP neural network model. In response to the problem of slow convergence speed of the model in error flat areas, the paper uses the Gaussian function to increase the learning rate of the gradient descent algorithm in error flat areas, and to control the learning rate of the gradient descent algorithm when the error is serious, which can effectively accelerate the convergence speed of the BP neural network model in error flat areas. This paper takes 6 river water level measurement stations in Jin an District, Fuzhou City as the research object, constructs a GA-BP neural network river water level prediction model for urban river water level prediction, and explores the impact of different rainfall input forms on the accuracy of river water level prediction. The results show that the GA-BP neural network can effectively improve the convergence speed and model prediction accuracy of the BP neural network in error flat areas. The Nash Efficiency Coefficient (NSE) of the experimental set prediction is above 0.8, and the relative error of the predicted peak water level can be controlled within 5%. The input of rainfall in the form of hourly rainfall can increase the predicted NSE to over 0.9. Research has shown that using Gaussian function to improve the BP neural network model can effectively improve the prediction accuracy of the model, which is of great significance to improving urban river water level prediction. [ABSTRACT FROM AUTHOR]