Advanced Time Series Models for Urban Drought: A Comparative Study of (S)ARIMA and Holt-Winters

Natural disasters, including droughts, exert a profound impact on public health, agriculture, industry, and the environment. The intensification of climate change has exacerbated the frequency and severity of such events. In the context of smart cities, the implementation of advanced prediction methodologies is pivotal for mitigating or even preventing these catastrophic outcomes. Among the most widely employed techniques for time series forecasting are the (S)ARIMA and Holt-Winters exponential smoothing methods, which form the foundation of this study. This research proposes a robust framework for the preprocessing of raw meteorological data, the application of forecasting techniques to monthly observations, and the evaluation of prediction models using standardized performance metrics. The objective is to identify the optimal model for predicting key parameters - such as air temperature, precipitation, and soil moisture - that serve as critical indicators of urban drought conditions. The findings reveal that the SARIMA model demonstrates superior reliability for time series exhibiting a decreasing trend, such as soil moisture, while the Holt-Winters method excels in modeling time series with no trend (e.g., precipitation) and those characterized by strong seasonality, such as air temperature. These insights contribute to the development of more effective urban drought prediction systems and highlight the nuanced applicability of these forecasting methods in addressing climate-related challenges.