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Potential Fishing Zone Characterization in the Indian Ocean by Machine Learning Approach

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1393))

Abstract

Potential fishing zone (PFZ) advisory is an essential guideline for the fishing community of India. Several studies have shown that high sea surface temperature (SST) gradient and high chlorophyll concentration in the ocean are the prospective areas for pelagic fish catch. ESSO-INCOIS provides advisories on PFZ on a daily basis using remotely sensed SST and chlorophyll-a data. The limitation of this advisory is that it does not give any information about the probable quantity of the fish. In this study, a hybrid decision tree model is developed for characterizing PFZ in the Indian Ocean. If SST gradient, persistence of SST gradient and chlorophyll concentration of any PFZ are given as the input variables, this model can classify the corresponding PFZ in terms of low, medium or high category of fish catch. According to this study, low SST gradient persistence and high SST gradient indicate possibility of high fish catch.

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Acknowledgments

The authors thank Director, INCOIS for supporting this work. Dr. Swarnali Majumder acknowledges Department of Science & Technology, Government of India, for financial support vide reference no. SR/WOS-A/EA3/2016 under Women Scientist Scheme to carry out this work.

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Authors and Affiliations

  1. Indian National Centre for Ocean Information Services, Pragathi Nagar, Nizampet, Hyderabad, 500090, India

    Swarnali Majumder, Sourav Maity, T. M. Balakrishnan Nair, Rose P. Bright, M. Nagaraja Kumar, Naga Shwetha & Nimit Kumar

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  1. Swarnali Majumder
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  2. Sourav Maity
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  3. T. M. Balakrishnan Nair
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  4. Rose P. Bright
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  5. M. Nagaraja Kumar
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  6. Naga Shwetha
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  7. Nimit Kumar
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Aruna Tiwari

  2. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Kapil Ahuja

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Majumder, S. et al. (2021). Potential Fishing Zone Characterization in the Indian Ocean by Machine Learning Approach. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_4

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