FISH PRODUCTION MODELING AND FORECASTING IN INDIA USING THE XGBOOST ALGORITHM

Shikha Yadav, Binita Kumari, Divya Sharma, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Yashpal Singh Raghav, Promil Kapoor, Tufleuddin Biswas, Soumik Ray, Aditya Pratap Singh, Pradeep Mishra

doi:https://doi.org/10.36899/JAPS.2025.2.0043

Article Abstract

Volume 35, No. (2), 2025 (April)

FISH PRODUCTION MODELING AND FORECASTING IN INDIA USING THE XGBOOST ALGORITHM

Shikha Yadav, Binita Kumari, Divya Sharma, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Yashpal Singh Raghav, Promil Kapoor, Tufleuddin Biswas, Soumik Ray, Aditya Pratap Singh, Pradeep Mishra

S. Yadav¹, B. Kumari², D. Sharma³, B. M. Alshaib⁵, Y. S. Raghav⁶, P. Kapoor⁷, T. Biswas⁸, S. Ray⁹*, A. P. Singh¹⁰, P. Mishra¹¹

¹ Miranda House, Department of Geography, University of Delhi, India,
² Department of Agricultural Economics, Rashtriya Kisan Post Graduate College, Shamli, Uttar Pradesh, India,
³ Central institute of Coastal Engineering for Fisheries, Department of Fisheries, Ministry of Fisheries Animal Husbandry and Dairying. Govt. of India, 560013, India,
⁴ Department of Banking and Insurance, Faculty of Economics, Damascus University, Damascus, Syrian Arab Republic,
⁵ Department of Banking and Insurance, Faculty of Economics, Damascus University, Damascus, Syrian Arab Republic,
⁶ Kingdom of Saudi Arabia, Department of Mathematics, College of Science, Jazan University, P.O.Box,114, Jazan 45142,
⁷ Chaudhary Charan Singh Haryana Agricultural University, Hisar 125 004, Haryana, India,
⁸ Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune-411004, India,
⁹ Department of Agricultural Economics and Statistics, Centurion University of Technology and Management, Paralakhemundi, Odisha, India,
¹⁰ Department of Plant Breeding and Genetics, School of Agriculture, GIET University, Gunupur-765022, Rayagada, Odisha, India,
¹¹ College of Agriculture, Rewa, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Hoshangabad, Madhya Pradesh, India,

Corresponding Author: raysoumik4@gmail.com

DOI: https://doi.org/10.36899/JAPS.2025.2.0043

Page Number(s): 522-530

Published Online First: March 12, 2025

Publication Date: April 28, 2025

ABSTRACT

Time series analysis using machine learning is vital for forecasting in commodity sciences. This research leverages advanced machine learning models for time series forecasting of fish production at both state and national levels in India. The study developed and compared traditional models, like the autoregressive integrated moving average (ARIMA) and state space models, with the advanced machine learning model, extreme gradient boosting (XGBoost), using training and test data sets. Results showed that XGBoost and state space models significantly outperformed the ARIMA model. Specifically, XGBoost had the highest accuracy in eight of eighteen series, followed by state space models (seven out of eighteen), and ARIMA models (three out of eighteen). This confirms that applying diverse machine learning models can enhance forecasting accuracy for fish production. After identifying the best-performing models, forecasts for fish production were extended to 2030, indicating that India’s total and marine fish production would likely continue to grow, with minimal change expected in key producing states. This data-driven analysis offers valuable insights for food security planning and policy-making in the region.

Keywords: Fish production, Time series, machine learning, forecasting, ARIMA, XGBoost

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Journal Impact Factor: 0.5 | (JCR Year: 2025) | Cite Score: 1.3

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Print ISSN: 1018-7081

Electronic ISSN: 2309-8694

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