Using Deep Learning Techniques to Forecast Environmental Consumption Level

Donghyun Lee, Suna Kang and Jungwoo Shin
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Donghyun Lee: Assistant professor, Department of Business Administration, Korea Polytechnic University, 237 Sangidaehak-ro, Siheung, Gyeonggi 15073, Korea
Suna Kang: Visiting Researcher, Korea Environment Institute, 370 Sicheong-daero, Sejong 30147, Korea
Jungwoo Shin: Assistant professor, Department of Industrial and Management Systems Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin, Gyeonggi 17104, Korea

Sustainability, 2017, vol. 9, issue 10, 1-17

Abstract: Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN) model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

Keywords: artificial intelligence; artificial neural network; consumption index; deep-learning technology; pro-environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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