  Drought is a serious natural disaster that has a long duration and a wide
range of influence. To decrease the drought-caused losses, drought prediction
is the basis of making the 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 or not 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 no single model
can achieve the best performance for all evaluation metrics simultaneously,
which indicates the drought prediction problem is still challenging. As
benchmarks for further studies, the code and results are publicly available in
a Github repository.
