System for Urban Construction and Model Optimization Based on Machine Learning Algorithms

Urban construction plays a vital role in the development of modern society, has a profound impact on the quality of life of residents, and promotes sustained economic growth. However, in the face of many complex and changeable factors in urban development, traditional planning methods often seem inadequate. This paper discusses the application of machine learning (ML) algorithm in urban construction and model optimization. The urban construction model optimization system based on ML algorithm makes full use of historical and real-time data as well as external factors and expert knowledge to automatically adjust model parameters and design schemes, thus improving the efficiency and quality of urban construction. This system shows significant advantages in population forecasting, land use optimization and traffic flow forecasting. Compared with traditional methods, it can provide more accurate population prediction results, more optimized land use planning and more accurate traffic flow prediction. These advantages help to enhance the scientific and accurate urban planning, and ultimately promote the sustainable development of cities. In terms of traffic flow prediction, the performance of ML algorithm is also very good. The mean square error of the algorithm is only 1200.5, which indicates that the average error between the predicted results and the actual traffic flow is very small. At the same time, the Mean absolute error is 65.4, which means that the error between the predicted value and the actual value of the ML algorithm is small. An accuracy of 81.5% further demonstrates the system's superior accuracy in traffic flow prediction. These data and results together demonstrate the important role of ML algorithm in urban construction and planning, and provide strong support and guidance for the sustainable development of cities.