Affiliations: [a]
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| [b]
Department of Statistics, University of California-Los Angeles, Los Angeles, USA
Correspondence:
[*]
Corresponding author. Weiwei Jiang, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. E-mail: [email protected].
Abstract: Drought is a serious natural disaster that has a long duration and a wide range of influences. To decrease drought-induced losses, drought prediction is the basis of corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study, and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 deep learning models are evaluated and compared. The results show that no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates that the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a GitHub repository.
Keywords: Drought prediction, weather data, machine learning, deep learning
