With the development of social economy, the public's awareness of participating in the natural protection of the environment has gradually increased, and a certain organizational structure and interest expression mechanism have gradually formed in the natural protection of the environment. The public's participation in natural environment protection is also increasingly concerned by government departments, social organizations and enterprises. Based on the random forest model, combined with public participation activities and management practice experience, this paper explores the problems and solutions of public participation in natural protection environment. The results show that: in the questionnaire survey of public environmental awareness, 164 people said "very important", 92.1%; Thirteen respondents thought it was "important", 7.3% of all respondents. There are 177 items in total, about 99.4% of all respondents. This shows that most people are still aware of environmental protection, but lack guidance.

Alsharifa Hind Mohammad. Nature Conservation Environment Based on Public Participation of Random Forests. Nature Environmental Protection (2023), Vol. 4, Issue 1: 20-30. https://doi.org/10.38007/NEP.2023.040103.

[1] Taiane Ritta Coelho, Marlei Pozzebon, Maria Alexandra Cunha. Citizens Influencing Public Policy-Making: Resourcing as Source of Relational Power in E-Participation Platforms. Inf. Syst. J. (2022) 32(2): 344-376. https://doi.org/10.1111/isj.12359

[2] Ocean Wu, Yun Sing Koh, Gillian Dobbie, Thomas Lacombe. Probabilistic Exact Adaptive Random Forest for Recurrent Concepts in Data Streams. Int. J. Data Sci. Anal. (2022) 13(1): 17-32. https://doi.org/10.1007/s41060-021-00273-1

[3] Jonathan Aaron Cook. Sample-Selection-Adjusted Random Forests. Int. J. Data Sci. Anal. (2022) 14(4): 375-388. https://doi.org/10.1007/s41060-022-00337-w

[4] Imad Bou-Hamad, Abdel Latef Anouze, Ibrahim H. Osman. A Cognitive Analytics Management Framework to Select Input and Output Variables for Data Envelopment Analysis Modeling of Performance Efficiency of Banks Using Random Forest and Entropy of Information. Ann. Oper. Res. (2022) 308(1): 63-92. https://doi.org/10.1007/s10479-021-04024-0

[5] Mingxue Jiang, Youlong Yang, Haiquan Qiu. Fuzzy Entropy and Fuzzy Support-Based Boosting Random Forests for Imbalanced Data. Appl. Intell. (2022) 52(4): 4126-4143. https://doi.org/10.1 007/s10489-021-02620-y

[6] Sangwon Kim, Mira Jeong, ByoungChul Ko.Lightweight Surrogate Random Forest Support for Model Simplification and Feature Relevance. Appl. Intell. (2022) 52(1): 471-481. https://doi.org/10. 1007/s10489-021-02451-x

[7] Anne-Christin Hauschild, Marta Lemanczyk, Julian O Matschinske, Tobias Frisch, Olga I Zolotareva, Andreas Holzinger, Jan Baumbach, Dominik Heider. Federated Random Forests can Improve Local Performance of Predictive Models for Various Healthcare Applications. Bioinform. (2022) (8): 2278-2286. https://doi.org/10.1093/bioinformatics/btac065

[8] Fahimeh Motamedi, Horacio Pérez Sánchez, Alireza Mehridehnavi, Afshin Fassihi, Fahimeh Ghasemi. Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies. Bioinform. (2022) 38(2): 469-475. https://doi.org/10.1093/bioinformatics/btab659

[9] Jae Yong Ryu, Jeong Hyun Lee, Byung Ho Lee, Jin Sook Song, Sunjoo Ahn, Kwang-Seok Oh. PredMS: A Random Forest Model for Predicting Metabolic Stability of Drug Candidates in human Liver Microsomes. Bioinform. (2022) 38(2): 364-368. https://doi.org/10.1093/bioinform atics/btab547

[10] Jean-Michel Nguyen, Pascal Jézéquel, Pierre Gillois, Luisa Silva, Faouda Ben Azzouz, Sophie Lambert-Lacroix, Philippe P Juin, Mario Campone, Aurélie Gaultier, Alexandre Moreau-Gaudry, Daniel Antonioli. Random Forest of Perfect Trees: Concept, Performance, Applications and Perspectives. Bioinform. (2021) 37(15): 2165-2174. https://doi.org/10.1093/bioinformatics/b tab074

[11] Pezhman Gholamnezhad, Ali Broumandnia, Vahid Seydi. An Inverse Model-Based Multiobjective Estimation of distribution Algorithm Using Random-Forest Variable Importance Methods. Comput. Intell. (2022) 38(3): 1018-1056. https://doi.org/10.1111/coin.12315

[12] Gerhard Tutz. Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles. J. Classif. (2022) 39(2): 241-263. https://doi.org/10.1007/s00357-021- 09406-4

[13] Valeria D'Amato, Rita Laura D'Ecclesia, Susanna Levantesi. ESG Score Prediction through Random Forest Algorithm. Comput. Manag. Sci. (2022) 19(2): 347-373. https://doi.org/10.1 007/s10287-021-00419-3

[14] Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas. Conclusive Local Interpretation Rules for Random Forests. Data Min. Knowl. Discov. (2022) 36(4): 1521-1574. https://doi.org/10.1007/s 10618-022-00839-y

[15] N. Bharatha Devi. Satellite Image Retrieval of Random Forest (rf-PNN) Based Probablistic Neural Network. Earth Sci. Informatics. (2022) 15(2): 941-949. https://doi.org/10.1007/s 12145-021-00759-3

[16] Josalin Jemima J, D Nelson Jayakumar, S Charles Raja, Venkatesh P. Proposing a Hybrid Genetic Algorithm based Parsimonious Random Forest Regression (H-GAPRFR) Technique for Solar Irradiance Forecasting with Feature Selection and Parameter Optimization. Earth Sci. Informatics. (2022) 15(3): 1925-1942. https://doi.org/10.1007/s12145-022-00839-y

[17] Tatsuki Kurihara, Nozomu Togawa. Hardware-Trojan Detection Based on the Structural Features of Trojan Circuits Using Random Forests. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. (2022) 105-A(7): 1049-1060. https://doi.org/10.1587/transfun.2021EAP1091

[18] Mirna Nachouki, Mahmoud Abou Naaj. Predicting Student Performance to Improve Academic Advising Using the Random Forest Algorithm. Int. J. Distance Educ. Technol. (2022) 20(1): 1-17. https://doi.org/10.4018/IJDET.296702