JOURNAL ARTICLE

An Economic Approach to Predict Biomass Level of Bangladesh Sundarbans Region Using Fuzzy Inference System.

  • Published In: New Mathematics & Natural Computation, 2023, v. 19, n. 3. P. 737 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Pujaru, Kanisha; Jana, Soovoojeet; Khatua, Anupam; Adak, Sayani; Kar, T. K. 3 of 3

Abstract

Seas, marine, and coastal regions are integral and essential parts of our ecosystem. Many scientific approaches have been taken to ensure the sustainable use of marine resources. Artificial intelligence (AI) plays a vital role in harvesting resources so that the system regenerates itself for the long term. This paper develops a two-input and two-output fuzzy logic-based model to predict the fisheries' remaining biomass after harvesting and maintaining a high revenue level in the Bangladesh Sundarbans region. Fishing & tourism are taken as input parameters, and revenue & biomass are taken as output parameters. A total of 20 rules (IF-THEN type) have been generated in the fuzzy rule editor of Fuzzy Inference System (FIS), considering all possible combinations between input–output parameters. The data which we obtained from the real ecosystem exactly corresponds to the results that we got from our proposed model. Our fuzzy logic model yields valid predictions of the remaining biomass level without compromising profit, only by controlling the harvesting and tourist entry. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:New Mathematics & Natural Computation. 2023/11, Vol. 19, Issue 3, p737
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:1793-0057
  • DOI:10.1142/S1793005723500321
  • Accession Number:174444833
  • Copyright Statement:Copyright of New Mathematics & Natural Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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