A Novel Hybrid Approach To Drought Forecasting: Leveraging Feature Engineering And Ensemble Methods

Area of Science:

• Environmental Science
• Climate Science
• Data Science

Background:

• Traditional drought forecasting methods struggle with complex, nonlinear climate patterns influenced by global warming and hazardous gases.
• Accurate drought prediction is essential for agricultural sustainability and disaster management, necessitating advanced computational techniques.

Purpose of the Study:

• To develop and evaluate an advanced machine learning model for improved drought prediction accuracy.
• To explore the effectiveness of feature engineering and ensemble methods in enhancing drought forecasting capabilities.

Main Methods:

• Utilized historical meteorological and environmental data for machine learning model development.
• Employed feature selection and engineering to create new predictive variables.
• Implemented a hybrid approach combining engineered features with machine learning ensemble classifiers for drought classification.

Main Results:

• The developed Hybrid Drought Forecasting Model demonstrated superior accuracy, precision, and F1 scores compared to previous research.
• The study validated the effectiveness of the selected features and ensemble techniques in improving drought prediction.
• The model successfully improved the ability to analyze climate trends and classify drought severity.

Conclusions:

• Advanced machine learning, particularly with engineered features and ensemble methods, offers a significant improvement over traditional drought forecasting.
• The developed hybrid model provides a robust tool for more accurate drought prediction and severity assessment.
• This approach enhances climate change adaptation strategies and disaster preparedness.