Machine learning method using to classify the intensity of drought in mountainous areas

Abstract

Introduction. This article presents research focused on classifying drought intensity in mountainous regions using advanced machine learning techniques. Due to the increasing frequency of climate-related disasters, understanding and predicting drought patterns have become crucial for environmental management and resource allocation. Purpose of the research. The purpose of this research is to develop a model that accurately classifies the intensity of droughts in mountainous areas, utilizing a diverse set of meteorological parameters to enhance prediction accuracy. Materials and Methods. The study employs the Random Forest algorithm, which is particularly suited for handling complex and non-linear relationships in climate data. A unique dataset was compiled, incorporating various meteorological factors, including temperature variations, precipitation levels, and wind speed, to train the model. The model’s performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Results. The proposed model achieved an impressive accuracy of 94%, demonstrating its effectiveness in predicting drought conditions. The analysis revealed that surface pressure and specific humidity had the most significant impact on prediction outcomes, indicating their relevance in drought assessments. Discussion. The findings highlight the limitations of traditional drought monitoring methods in mountainous climates. The enhanced model provides more accurate forecasts, which can be crucial for stakeholders in agriculture, water management, and disaster preparedness. Conclusion. The research successfully demonstrates the potential of machine learning for improved drought classification in challenging terrains. It offers a robust framework for understanding the intricacies of climate impacts in mountainous regions. Resume. This study contributes valuable insights into the intersection of machine learning and environmental science, showcasing the importance of innovative approaches for effective climate risk management. Suggestions for practical applications and directions for future research. The developed model can be utilized by policymakers and resource managers to make informed decisions regarding water conservation and agricultural practices. Future research could explore the integration of additional climate variables and the application of the model in different geographic locations to generalize its effectiveness.

Topics & Concepts