Integration of Computer Simulation and Bim in Water Conservancy Flood Control Simulation and Early Warning System

This paper proposes an innovative flood control simulation and early warning system integrating computer simulation and building information modeling (BIM). The system combines deep learning technology with water flow simulation, uses long short-term memory network (LSTM) to model and predict historical water level data, and combines the three-dimensional terrain information provided by the BIM model to dynamically evaluate the water flow and flood spread risks. Through this innovative algorithm, the system can achieve accurate flood prediction according to different scenarios and conditions, and improve the timeliness and accuracy of early warning. In the experimental part, a reservoir was used as a case for verification. The results showed that the LSTM-based algorithm improved the prediction accuracy by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 0 \%}$</tex> compared with the traditional regression prediction method, and the computational efficiency was improved by 15 %. Compared with traditional methods, the water conservancy flood control early warning system integrating BIM and deep learning is more stable and efficient under complex terrain. This study provides new ideas for intelligent decision-making in the field of water conservancy and flood control. It is not only highly practical, but also provides technical support for further promoting intelligent water conservancy management and disaster prevention and control.