Construction of a Meteorological Data Support Vector Machine Drought Prediction Model Based on Machine Learning Algorithms

In recent years, the proliferation of machine learning algorithms in drought prediction has revolutionized the field, leveraging their robust data processing and pattern recognition abilities to offer fresh perspectives and methodologies. This article specifically employs machine learning to develop a support vector machine (SVM) drought prediction model grounded in meteorological data, aiming to enhance prediction accuracy. Following a comprehensive literature review outlining the fundamentals of machine learning and its applications in drought forecasting, the article delves into the construction process of the SVM-based model in detail. Experimental analysis validates the model’s strengths, revealing that despite Decision Tree and Linear Regression algorithms demonstrating improved accuracy trends (ranging from 0.7-0.86 and 0.65-0.87, respectively), the SVM algorithm consistently outperforms them, achieving superior prediction accuracy. This underscores the conclusion that the SVM algorithm is a highly effective tool for drought prediction, offering precision that surpasses traditional methods.