IDEAS home Printed from https://ideas.repec.org/a/wly/jnlamp/v2021y2021i1n8520633.html

Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model

Author

Listed:
  • Bingjie Zhang
  • Yiming Liu

Abstract

With the rapid development of information society, a large amount of vague or uncertain English interpretation information appears in daily life. Uncertain information processing is an important research content in the field of artificial intelligence. In this paper, we combine the three‐branch concept lattice and linguistic values with the digital elevation model and propose the three‐branch fuzzy linguistic concept lattice as well as the attribute approximation method. In this paper, an improved serial algorithm for sink accumulation is proposed. The improved algorithm changes the order of cell calculation; after the cumulative amount of a “sub‐basin” is calculated, all cells of the next “sub‐basin” are calculated until all cells are calculated. The improved algorithm reduces the overhead space in the calculation process, reduces the pressure of cells entering and leaving the queue, and improves the calculation efficiency. The improved cumulant algorithm is compared with the commonly used recursive cumulant algorithm and the nonrecursive cumulant algorithm, and the improved algorithm improves by about 17% compared with the nonrecursive algorithm at 106 cell level, and the computation time of the recursive algorithm is about 3 times of the improved algorithm. Because the sink accumulation serial algorithm is an important part of the parallel calculation of sink accumulation, and the execution time is shorter by using the improved algorithm, this paper applies the proposed improved accumulation serial algorithm to the process of parallel calculation of accumulation.

Suggested Citation

  • Bingjie Zhang & Yiming Liu, 2021. "Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model," Advances in Mathematical Physics, John Wiley & Sons, vol. 2021(1).
    • Handle: RePEc:wly:jnlamp:v:2021:y:2021:i:1:n:8520633
    DOI: 10.1155/2021/8520633
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2021/8520633
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/8520633?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rodrigo L. Soteres & Javier Pedraza & Rosa M. Carrasco, 2020. "Snow avalanche susceptibility of the Circo de Gredos (Iberian Central System, Spain)," Journal of Maps, Taylor & Francis Journals, vol. 16(2), pages 155-165, December.
    2. Guilherme Garcia Oliveira & Luis Fernando Chimelo Ruiz & Laurindo Antonio Guasselli & Claus Haetinger, 2019. "Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 1049-1073, November.
    3. Anuj Tiwari & Mohammad Shoab & Abhilasha Dixit, 2021. "GIS-based forest fire susceptibility modeling in Pauri Garhwal, India: a comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1189-1230, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zühal Özcan & İnci Caglayan & Özgür Kabak & Fatmagül Kılıç Gül, 2025. "Integrated risk mapping for forest fire management using the analytical hierarchy process and ordered weighted average: a case study in southern Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(1), pages 959-1001, January.
    2. Hongrui Wen & Qiaozhen Guo & Yuhuai Zeng & Zepeng Wu & Zhenhui Sun, 2022. "Study on forest fire risk in Conghua district of Guangzhou city based on multi-source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3163-3183, December.
    3. Rui Yuan & Jing Chen, 2022. "A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1393-1426, November.
    4. Sukanta Malakar & Abhishek K. Rai & Arun K. Gupta, 2023. "Earthquake risk mapping in the Himalayas by integrated analytical hierarchy process, entropy with neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 951-975, March.
    5. Paulo Rodolpho Pereira Hader & Fábio Augusto Gomes Vieira Reis & Anna Silvia Palcheco Peixoto, 2022. "Landslide risk assessment considering socionatural factors: methodology and application to Cubatão municipality, São Paulo, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 1273-1304, January.
    6. Iraj Rahimi & Lia Duarte & Wafa Barkhoda & Ana Cláudia Teodoro, 2025. "Comparative Analysis of Non-Negative Matrix Factorization in Fire Susceptibility Mapping: A Case Study of Semi-Mediterranean and Semi-Arid Regions," Land, MDPI, vol. 14(7), pages 1-21, June.
    7. Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motirh Al-Mutiry, 2022. "GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    8. Tingyu Zhang & Quan Fu & Chao Li & Fangfang Liu & Huanyuan Wang & Ling Han & Renata Pacheco Quevedo & Tianqing Chen & Na Lei, 2022. "Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3327-3358, December.
    9. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3797-3816, April.
    10. Yu Chen, 2025. "Spatial prediction and mapping of landslide susceptibility using machine learning models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(7), pages 8367-8385, April.
    11. Abdel Rahman Al-Shabeeb & Ibraheem Hamdan & Sedigheh Meimandi Parizi & A’kif Al-Fugara & Sana’a Odat & Ismail Elkhrachy & Tongxin Hu & Saad Sh. Sammen, 2023. "A Comparative Study of Genetic Algorithm-Based Ensemble Models and Knowledge-Based Models for Wildfire Susceptibility Mapping," Sustainability, MDPI, vol. 15(21), pages 1-25, November.
    12. Yucel Gulluce, 2021. "A LabVIEW-based fire monitoring software using multi-criteria AHP approach for detecting geolocation of wildfire," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1849-1876, November.
    13. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11485-11525, October.
    14. Mohd Amin Khan & Amitesh Gupta & Pritee Sharma & Arijit Roy, 2026. "Investigation of wildfire risk and its mapping using GIS-integrated AHP method: a case study over Hoshangabad Forest Division in Central India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 28(4), pages 8681-8715, April.
    15. Xiao-yan Huang & Li He & Hua-sheng Zhao & Ying Huang & Yu-shuang Wu, 2021. "Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 331-353, May.
    16. Mária Barančoková & Matej Šošovička & Peter Barančok & Peter Barančok, 2021. "Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone," Land, MDPI, vol. 10(12), pages 1-28, December.
    17. Bilal Aslam & Adeel Zafar & Umer Khalil, 2025. "Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(10), pages 24723-24750, October.
    18. Youssef El Miloudi & Younes El Kharim & Ali Bounab & Rachid El Hamdouni, 2024. "Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco)," Land, MDPI, vol. 13(2), pages 1-16, February.
    19. Tamali Mondal & Soumya Dasgupta & Dinesh Chandra Bhatt & K. Ramesh, 2024. "Natural or man-made? Finding the drivers of forest fires within the protected area habitats: a case study from the lower Shivalik Landscape, Western Himalaya," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(8), pages 7765-7785, June.
    20. Xudong Hu & Hongbo Mei & Han Zhang & Yuanyuan Li & Mengdi Li, 2021. "Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1663-1689, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jnlamp:v:2021:y:2021:i:1:n:8520633. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/3197 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.