An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data

TL;DR

This study evaluates various machine learning and deep learning models for drought prediction using weather data, highlighting the challenge in achieving consistently accurate predictions and providing benchmarks for future research.

Contribution

It systematically compares 16 machine learning and 16 deep learning models for drought prediction using real weather data, establishing benchmarks for future work.

Findings

No single model outperforms others across all metrics

Drought prediction remains a challenging problem

Code and results are publicly available for reproducibility

Abstract

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.