Enhancing drought resilience: machine learning-based vulnerability assessment in Uttar Pradesh, India

Area of Science:

• Environmental Science
• Climatology
• Data Science

Background:

• Drought is a complex climatic hazard with significant natural and social impacts.
• Assessing drought vulnerability is crucial for effective disaster risk reduction and management.

Purpose of the Study:

• To apply machine learning algorithms (MLAs) for assessing drought vulnerability (DVM) in Uttar Pradesh, India.
• To identify areas highly prone to drought using a comprehensive set of physical and meteorological factors.

Main Methods:

• Utilized 18 factors, categorized into physical and meteorological drought indicators.
• Employed artificial neural networks (ANNs) for DVM assessment.
• Validated model performance using the receiver operating characteristic curve (ROC) analysis.

Main Results:

• Identified that 31.38% of Uttar Pradesh, particularly the eastern region, is highly to very highly prone to drought.
• The artificial neural network model demonstrated strong performance with an Area Under the Curve (AUC) value of 0.843.
• The study provides a data-driven approach to mapping drought-vulnerable zones.

Conclusions:

• Machine learning-based drought vulnerability maps are valuable tools for informing policy decisions.
• Findings highlight the urgent need for targeted drought mitigation and adaptation strategies in vulnerable areas of Uttar Pradesh.
• Future research should focus on refining MLA models and integrating socio-economic data for enhanced drought resilience.