Predicting Food Supply with LSTM: A Data-Driven Approach Using WASDE Data

DOI

10.2991/978-94-6463-587-4_44How to use a DOI?

Keywords

Food Supply Forecasting; LSTM; Time Series Analysis; WASDE Data

Abstract

Ensuring a stable and sustainable food supply is a critical global challenge. Accurate forecasting of food supply is essential for effective policymaking, resource allocation, and risk management. This study proposes a novel approach to food supply forecasting using a Long Short-Term Memory (LSTM) neural network model, leveraging the World Agricultural Supply and Demand Estimates (WASDE) dataset. The study aims to develop a robust LSTM model capable of accurately predicting food supply and evaluating its performance using appropriate metrics. The LSTM model was trained on historical WASDE data, which includes information on production, consumption, and ending stocks for various commodities. The model’s performance was evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the LSTM model achieved varying levels of success in predicting food supply for different commodities. For some commodities, such as rice and coarse grain, the model demonstrated strong accuracy with low RMSE and MAE values. However, for other commodities, like corn and wheat, the model struggled to accurately predict supply, especially during periods of high volatility. In conclusion, this study demonstrates the potential of LSTM models in food supply forecasting, utilizing the WASDE dataset. While the model achieved promising results for certain commodities, further research is needed to improve its accuracy for more challenging commodities. Future studies could explore incorporating additional factors, such as climate change and geopolitical events, to enhance the model’s predictive capabilities.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - I Made Dwi Jendra Sulastra
AU  - I Putu Oka Wisnawa
AU  - I Komang Wiratama
AU  - I Putu Astya Prayudha
PY  - 2024
DA  - 2024/12/01
TI  - Predicting Food Supply with LSTM: A Data-Driven Approach Using WASDE Data
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2024 (ICoSTAS-EAS 2024)
PB  - Atlantis Press
SP  - 385
EP  - 394
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-587-4_44
DO  - 10.2991/978-94-6463-587-4_44
ID  - Sulastra2024
ER  -