There is a certain conflict between urban development and natural protection. To coordinate the relationship between them and promote their common healthy development is one of the most important hot issues in the current research field. On the one hand, it is necessary to ensure that people and natural environmental systems can live in harmony; on the other hand, the damage caused to the natural environment by the economic and social construction process cannot be ignored. These two aspects not only depend on whether to meet the needs of contemporary human beings without endangering the living space of future generations, but also depend on various conflicts caused by the development and utilization of natural environmental resources and the production activities of urbanization. In order to solve the problems of the traditional natural environment protection model, such as the failure to implement the urban development and governance policy in time in the process of environmental pollution control, the excessive reliance on the professional knowledge and subjective judgment of experts, the inability to objectively evaluate the state of the natural environment, and the difficulty in collecting samples in the natural environment, this paper proposed a natural environment protection model based on machine learning and combined with random forest regression algorithm. Through the comparative analysis of the experimental results, the innovative natural environment protection model had an average improvement of 10.0% in four aspects compared with the traditional natural environment protection model. 

Olcese Umberto. Coordinated Development of Urban Development and Natural Protection Environment Based on Machine Learning. Nature Environmental Protection (2021), Vol. 2, Issue 4: 48-57. https://doi.org/10.38007/NEP.2021.020406.

[1]Seddon, Nathalie. "Getting the message right on nature‐based solutions to climate change." Global change biology 27.8 (2021): 1518-1546.

[2]Whitburn, Julie, Wayne Linklater, and Wokje Abrahamse. "Meta‐analysis of human connection to nature and proenvironmental behavior." Conservation biology 34.1 (2020): 180-193.

[3]Hanson, Jeffrey O. "Global conservation of species’ niches." Nature 580.7802 (2020): 232-234.

[4]Hemingway, Jordon D. "Mineral protection regulates long-term global preservation of natural organic carbon." Nature 570.7760 (2019): 228-231.

[5]Bossio, D. A. "The role of soil carbon in natural climate solutions." Nature Sustainability 3.5 (2020): 391-398.

[6]Fahrig L. "Environmental challenges for the Belt and Road Initiative." Nature Sustainability 1.5 (2018): 206-209.

[7]Molinario, Erica. "Motivations to act for the protection of nature biodiversity and the environment: a matter of “Significance”." Environment and Behavior 52.10 (2020): 1133-1163.

[8]Shahzad, Tahir. "People’s attitude towards willingness-to-pay for environmental protection in Pakistan." Environmental Science and Pollution Research 29.35 (2021): 52635-52654.

[9]Ponitka, Jens, and Sarah Boettner. "Challenges of future energy landscapes in Germany—a nature conservation perspective." Energy, Sustainability and Society 10.1 (2020): 1-11.

[10]Higgins-Desbiolles, Freya. "Socialising tourism for social and ecological justice after COVID-19." Tourism Geographies 22.3 (2020): 610-623.

[11]Sadiq, Muhammad. "Linking nuclear energy, human development and carbon emission in BRICS region: Do external debt and financial globalization protect the environment?." Nuclear Engineering and Technology 54.9 (2021): 3299-3309.

[12]Stelmasiak, Jerzy. "Nature Reserve as a Legal Form of Nature Protection." Studia Iuridica Lublinensia 29.2 (2020): 163-172.

[13]Janiesch, Christian, Patrick Zschech, and Kai Heinrich. "Machine learning and deep learning." Electronic Markets 31.3 (2021): 685-695.

[14]Jiang, Tammy, Jaimie L. Gradus, and Anthony J. Rosellini. "Supervised machine learning: a brief primer." Behavior Therapy 51.5 (2020): 675-687.

[15]Zhang, Wengang, et al. "Assessment of pile drivability using random forest regression and multivariate adaptive regression splines." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 15.1 (2021): 27-40.