After years of efforts in ecological environment protection (EP), China has made great progress in environmental protection, but there are still some problems in our ecological EP. In this paper, the strategy of natural environment (NE) protection is studied and analyzed based on recurrent neural network. The principles of NE protection and the basic structure of recurrent neural network are briefly summarized; The design of the cyclic neural network(CNN) model is discussed, and the application of the CNN in air quality prediction(AQP) is analyzed; Taking Shanghai AQP as the research object, combined with information entropy and gray correlation analysis methods, relevant input variables were screened out to build the network model, which helped us to have a general understanding of the future pollution factor concentration from the overall situation, and then put forward NE protection strategies to achieve good and effective protection of the NE.

Eden Steven. Natural Environment Protection Strategy Based on Cyclic Neural Network. Nature Environmental Protection (2020), Vol. 1, Issue 2: 17-26. https://doi.org/10.38007/NEP.2020.010203.

[1] Varun Mishra, Florian Künzler, Jan-Niklas Kramer, Elgar Fleisch, Tobias Kowatsch, David Kotz. Detecting Receptivity for mHealth Interventions in the Natural Environment. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (2020) 5(2): 74:1-74:24. https://doi.org/10.1145/3 463492

[2] Juanying Xie, Yinyuan Lu, Zhaozhong Wu, Shengquan Xu, Phil W. Grant. Investigations of Butterfly Species Identification from Images in Natural Environments. Int. J. Mach. Learn. Cybern. (2020) 12(8): 2431-2442. 

[3] Yuta Yaguchi, Kenji Takeuchi, Tadashi Waragai, Toshitake Tateno. Durability Evaluation of An Additive Manufactured Biodegradable Composite with Continuous Natural Fiber in Various Conditions Reproducing Usage Environment. Int. J. Autom. Technol. (2020) 14(6): 959-965. https://doi.org/10.20965/ijat.2020.p0959

[4] Victor Chang, Pin Ni, Yuming Li. K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index. IT Prof. (2020) 22(4): 28-34. https://doi.org/10.1109/MITP.2020.2993851

[5] Ali Akbari, Roger Solis Castilla, Roozbeh Jafari, Bobak J. Mortazavi. Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments. IEEE J. Biomed. Health Informatics. (2020) 24(9): 2639-2650. https://doi.org/ 10.1109/JBHI. 2020.2966151

[6] Euisung Jung, Eun Ju Jung. Service-oriented Architecture of Environmental Information Systems to Forecast the Impacts of Natural Disasters in South Korea. J. Enterp. Inf. Manag. (2015) 32(1): 16-35. https://doi.org/10.1108/JEIM-03-2015-0022

[7] George Margetis, Stavroula Ntoa, Margherita Antona, Constantine Stephanidis. Augmenting Natural Interaction with Physical Paper in Ambient Intelligence Environments. Multim. Tools Appl. (2019) 78(10): 13387-13433. https://doi.org/10.1007/s11042-018-7088-9

[8] Georgi Tinchev, Adrián Peñate Sánchez, Maurice F. Fallon. Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on A CPU. IEEE Robotics Autom. Lett. (2019) 4(2): 1327-1334. https://doi.org/10.1109/LRA.2019.2895264

[9] Daniel Bekele Wakeyo, Ajit Pal Singh, Muralidhar Avvari. Influence of Abattoir Waste Management Practices on Natural Environment Conservation. Int. J. Syst. Assur. Eng. Manag. (2019) 10(5): 1010-1022. https://doi.org/10.1007/s13198-019-00830-9

[10] Hui Wen Loh, Ooi Chui Ping, Shivani Dhok, Manish Sharma, Ankit A. Bhurane, U. Rajendra Acharya. Detection of Cyclic Alternating Pattern and Classification of Sleep Stages Using Deep Neural Network. Appl. Intell. (2020) 52(3): 2903-2917. 

[11] Morteza Hosseini, Nitheesh Kumar Manjunath, Bharat Prakash, Arnab Neelim Mazumder, Vandana Chandrareddy, Houman Homayoun, Tinoosh Mohsenin. Cyclic Sparsely Connected Architectures for Compact Deep Convolutional Neural Networks. IEEE Trans. Very Large Scale Integr. Syst. (2020) 29(10): 1757-1770. 

[12] Momeneh S, Nourani V. Application of A Novel Technique of the Multi-discrete Wavelet Transforms in Hybrid with Artificial Neural Network to Forecast the Daily and Monthly Stream flow. Modeling Earth Systems and Environment. (2020) 8(4):4629-4648. 

[13] Casallas A, Ferro C, Celis N. Long Short-Term Memory Artificial Neural Network Approach to Forecast Meteorology and PM2.5 Local Variables in Bogotá, Colombia. Modeling Earth Systems and Environment. (2020) 8(3):2951-2964. 

[14] Ighalo J O, Igwegbe C A, Adeniyi A G. Multi-layer Perceptron Artificial Neural Network (MLP-ANN) Prediction of Biomass Higher Heating Value (HHV) Using Combined Biomass Proximate and Ultimate Analysis Data. Modeling Earth Systems and Environment. (2020) 8(3):3177-3191. 

[15] Liang S, Zhong Q. Reducing Environmental Impacts Through Socioeconomic Transitions: Critical Review and Prospects. Frontiers of Environmental Science & Engineering. (2020) 17(2):1-20. 

[16] Mohanty S, Dash R. A Novel Chaotic Flower Pollination Algorithm for Modelling An Optimized Low-Complexity Neural Network-Based NAV Predictor Model. Progress in Artificial Intelligence. (2020) 11(4):349-366. 

[17] Gu J, Xiang R, Wang X. Multi-probe Attention Neural Network for COVID-19 Semantic Indexing. BMC Bioinformatics. (2020) 23(1):1-27. 

[18] Qiang Lai, Cong Lai, Paul Didier Kamdem Kuate, Chunbiao Li, Shaobo He. Chaos in A Simplest Cyclic Memristive Neural Network. Int. J. Bifurc. Chaos (2020) 32(3): 2250042:1-225 0042:16. https://doi.org/10.1142/S0218127422500420