Co-Evolutionary Fuzzy Deep Transfer Learning for Disaster Relief Demand Forecasting

Relief demand forecasting is vital to the success of disaster relief operations, but it is associated with challenges including insufficient training samples, incomplete and imprecise inputs, and inaccurate demands. This article presents a co-evolutionary fuzzy deep transfer learning (CoFDTL) method...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing 2022-07, Vol.10 (3), p.1361-1373
Main Authors: Zheng, Yu-Jun, Yu, Si-Lan, Song, Qin, Huang, Yu-Jiao, Sheng, Wei-Guo, Chen, Sheng-Yong
Format: Article
Language:English
Subjects:
co-evolutionary learning
Deep learning
Demand
Demand forecasting
Disaster relief
Disaster relief demands
Earthquake prediction
Earthquakes
Economic forecasting
Evolutionary algorithms
fuzzy learning
Machine learning
Mathematical models
Prediction algorithms
Pythagorean fuzzy sets
Sociology
Task analysis
Transfer learning
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Summary:Relief demand forecasting is vital to the success of disaster relief operations, but it is associated with challenges including insufficient training samples, incomplete and imprecise inputs, and inaccurate demands. This article presents a co-evolutionary fuzzy deep transfer learning (CoFDTL) method for relief demand forecasting where different types of disasters (e.g., earthquake, typhoon, and flood) are considered as different tasks. CoFDTL consists of three stages. First, a deep fuzzy learning model is used to learn latent representation of the shared inputs of all tasks. Second, a co-evolutionary algorithm is used to simultaneously learn task-specific features and the shared regressor. Third, the shared regressor is re-trained based on the best solutions obtained for different tasks in the second stage. Experiments demonstrate that CoFDTL exhibits significant performance improvements over the selected popular fuzzy learning, deep learning, and transfer learning models. This article also reports the application of CoFDTL to two real-world disasters in China, 2018. The proposed CoFDTL that integrates fuzzy deep learning, transfer learning, and co-evolutionary learning can be used for many other complex multi-task transfer learning problems with insufficient samples and uncertain information.
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2021.3085337