Abstract:
Global warming has intensified the frequency and severity of droughts worldwide, complicating
the characterization and continuous monitoring of regional drought conditions. Traditional
methods often struggle with the spatial variability of meteorological factors and the influence of
extreme weather events, leading to potential distortions in drought analysis. This thesis introduces
two innovative drought indices aimed at enhancing the accuracy and reliability of regional drought
monitoring: the Support Vector Machine-based Drought Index (SVM-DI) and the Auxiliary
Support Vector Machine Drought Index (Aux-SVMDI). The SVM-DI is developed by applying
different weights to an SVM-based X-bar control chart, incorporating regional precipitation
aggregate data. This new index, tested in northern Pakistan, demonstrates more pronounced
regional characteristics and a significantly lower Coefficient of Variation (CV) in its correlations
with other meteorological stations compared to the Regional Standard Precipitation Index (RSPI).
These findings highlight SVM-DI's capability to reduce the impact of extreme values and outliers,
making it a robust tool for regional drought analysis. Similarly, the Aux-SVMDI integrates
monthly precipitation estimates derived from auxiliary data and predicted errors using SVM,
combined with temperature as an auxiliary variable in a regression estimation framework. This
index employs control charts derived from SVM error estimations to enhance sensitivity in
detecting hydrological droughts. The results indicate that Aux-SVMDI provides superior estimates
by utilizing auxiliary information, thereby offering a more precise and reliable assessment of
hydrological drought conditions. Both SVM-DI and Aux-SVMDI represent significant
advancements in drought monitoring methodologies, offering enhanced tools for practitioners to
accurately define and manage regional climatology and drought conditions. Future research should
explore the integration of additional climatic variables and advanced machine learning techniques
to further refine these indices.
