Utilising machine learning classification models for meteorological drought monitoring and analysis

Abstract

This study identifies drought events using the Standardized Precipitation Index (SPI) and applies four machine learning model Support Vector Machines (SVM), Random Forest (RF), Gradient Boosting (GB), and Logistic Regression for drought prediction. Meteorological data from 12 stations across Punjab, Pakistan, covering northern, eastern, and central regions were utilised. Independent variables included average temperature, specific humidity, soil moisture, and dew point. To address model-specific challenges, ridge regression was applied to mitigate multicollinearity in Logistic Regression, while SVM incorporated the Radial Basis Function (RBF) kernel and isolation forest to manage non-linearity and outliers. RF and GB were implemented without additional modifications. The Synthetic Minority Oversampling Technique (SMOTE) was used to handle class imbalance. Model performance was assessed using accuracy, precision, recall, F1-score, specificity, and the area under the receiver operating characteristic curve (ROC-AUC). SVM demonstrated the highest predictive capability with an ROC-AUC of 0.8166, followed by Logistic Regression (0.8024), while RF and GB recorded values of 0.742 and 0.7422, respectively. These findings highlight the superior performance of SVM in drought prediction and emphasise the importance of model selection and data preprocessing in enhancing drought monitoring for improved water resource management and climate adaptation strategies.