Research on the optimization and intelligent assessment of garden landscape ecology driven by big data and machine learning

This article focuses on the application of big data and Machine learning (ML) technology in the optimization of garden landscape ecology. In order to achieve the research goal, this article constructs an optimization model based on Random forest (RF) algorithm. In the model design stage, the data of garden landscape ecology are analyzed in detail, and the key features that have a significant impact on the optimization objectives are screened out, and a reasonable structural framework and parameter configuration are formulated. Then, a large number of experimental data are obtained through a well-designed experimental scheme, and the actual effect of the optimization algorithm is comprehensively evaluated. The experimental results show that RF algorithm shows excellent prediction ability and practical application value in the optimization of garden landscape ecology. This algorithm significantly improves the vegetation coverage, promotes the improvement of water resources management, and strengthens the effect of biodiversity protection. The research shows that big data and ML technology have opened up a brand-new solution path for the optimization of garden landscape ecology, and show significant application potential. This study will help to achieve more accurate and efficient optimization of garden landscape ecology, and provide strong support for the sustainable development of urban environment.