A Time Series Forecasting Methodology for Climatic Drivers of Urban Drought in Sustainable Smart City Planning

Urban drought is a climate-related challenge that threatens environmental sustainability, public health, and socio-economic stability in urban areas. With increasing climate variability, sustainable smart city planning requires reliable forecasting methodologies to facilitate adaptive water resource management and long-term climate resilience plans. This study proposes and evaluates a time series forecasting methodology for the climatic drivers of urban drought, using standard statistical approaches—Seasonal Autoregressive Integrated Moving Average ((S)ARIMA) and Holt–Winters exponential smoothing. The methodology includes systematic preprocessing of meteorological data, univariate time series modeling, and performance evaluation using recognized accuracy metrics (RMSE, MAE, and MAPE). Air temperature, precipitation, soil moisture, and wind speed are analyzed as key climatic variables affecting urban drought dynamics. The results indicate that forecast performance varies based on the statistical characteristics of each variable: (S)ARIMA models provide superior predictive accuracy for series with significant seasonality or stochastic fluctuations, whereas the Holt–Winters method is more appropriate for variables displaying sustained downward trends, particularly soil moisture. The forecasts provide a methodological foundation for calculating drought indices and classifying severity, enhancing early warning capabilities and supporting sustainable smart city planning under increasing climate uncertainty.