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.
翻译:干旱是一种严重的自然灾害,具有长期和广泛影响。为了减少干旱造成的损失,干旱预测是制定相应的干旱预防和减灾措施的基础。虽然这个问题已在文献中研究过,但尚不清楚的是,利用气象数据机器学习模型是否能够准确预测干旱。为回答这一问题,本研究利用了一个真实世界的公共数据集,用18个气象指标的最后90天预测不同的干旱水平。在综合方法中,对16个机器学习模型和16个深层学习模型进行了评估和比较。结果显示,没有任何单一模型能够同时取得所有评估指标的最佳性能,这表明干旱预测问题仍然具有挑战性。作为进一步研究的基准,在Github存放处可以公开查阅代码和结果。