Fish production modeling and forecasting in india using the xgboost algorithm

S. Yadav; B. Kumari; A. M. G. Al khatib; Y. S. Raghav; D. Sharma; B. M. Alshaib; H. Nayak; S. Ray; T. Biswas; N. Mishra; P. Mishra

doi:10.36899/JAPS.2025.2.0043

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Fish production modeling and forecasting in india using the xgboost algorithm

Author(s)

S. Yadav Miranda House, Department of Geography, University of Delhi, India

B. Kumari Department of Agricultural Economics, Rashtriya Kisan Post Graduate College,Shamli, Uttar Pradesh,India

A. M. G. Al khatib Department of Finance and Banking, Faculty of Economics, Damascus University,Damascus,Syrian Arab Republic

Y. S. Raghav Department of Mathematics, College of Science, Jazan University,P.O. Box. 114, Jazan 45142, Kingdom of,Saudi Arabia

D. Sharma Department of Fisheries, Ministry of Fisheries Animal Husbandry and Dairying. Govt. of India,560013,India

B. M. Alshaib Department of Finance and Banking, Faculty of Economics, Damascus University,Damascus,Syrian Arab Republic

H. Nayak Department of Silviculture and Agroforestry, Odisha University of Agriculture and Technology, Bhubaneswar,India

S. Ray Department of Agricultural Economics and Statistics, Centurion University of Technology and Management,Paralakhemundi, Odisha,India

T. Biswas Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune-411004, India

N. Mishra Department of Mathematics and Statistics School of Physical Sciences, Amity University,Mohali, Punjab,India

P. Mishra College of Agriculture,Rewa, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Madhya Pradesh,India

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.

Publication Details

Page(s) 522-530

DOI 10.36899/JAPS.2025.2.0043

Published Journal: Journal of Animal and Plant sciences, Volume: 35, Issue: 2, Year: 2025

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Keywords

ARIMA, Forecasting, Fish production, machine learning, Time Series, XGBoost

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