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http://dx.doi.org/10.5392/JKCA.2018.18.11.416

A Prediction Model for Agricultural Products Price with LSTM Network  

Shin, Sungho (한국과학기술정보연구원 연구데이터플랫폼센터)
Lee, Mikyoung (한국과학기술정보연구원 연구데이터플랫폼센터)
Song, Sa-kwang (한국과학기술정보연구원 연구데이터플랫폼센터/과학기술연합대학원대학교 빅데이터과학과)
Publication Information
The Journal of the Korea Contents Association / v.18, no.11, 2018 , pp. 416-429 More about this Journal
Abstract
Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model's accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.
Keywords
Natural Disaster; Agricultural Product; Price; Prediction; Deep Learning; LSTM; RMSE; Weather;
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