Optimal design of urban drainage system based on GIS and machine learning

How to optimize multi-objective drainage efficiently and accurately is a key problem in urban drainage. In response to this problem, in this paper, a novel method combining geographic information System (GIS), Random Forest Regression (RFR) and Multi-Objective Optimization is proposed. Multi-Objective Forest Regression (MOFR) method provides detailed spatial data and terrain information through GIS, and RFR realizes high-precision prediction of urban drainage system performance, and uses MOO for multi-objective optimization, so as to ensure high prediction accuracy and improve the comprehensive performance of the system. Ultimately, experimental results, obtained through comparative experiments and analysis with traditional machine learning methods, demonstrate that the MOFR method exhibits notable advantages in enhancing prediction accuracy, minimizing function errors, and optimizing water quantity testing. Especially in complex multi-objective optimization scenarios, MOFR method can more accurately find the optimal design scheme and reduce the uncertainty in the decision-making process. This fully verifies the reliability and effectiveness of the architecture in urban drainage system optimization design.