An impact-based drought classification method using real-world agricultural drought records and explainable automated machine learning

Key Points

• The method offers a new way to classify drought, focused on its agricultural impacts, rather than traditional metrics.
• Employing automated machine learning techniques enhances classification accuracy by leveraging real-world agricultural drought records.
• Using explainable AI ensures the understanding of model decisions, potentially aiding in stakeholder strategy development.
• This approach highlights the importance of integrating real-world data into drought management and policy planning.