Regional drought forecasting in data-scarce regions through a novel cluster-based liquid neural network

forecasts are ultimately extended across all GDM points in the corresponding homogenous region to map future regional drought patterns. The new approach was demonstrated through a case study conducted for the Lake Urmia Basin (LUB), which suffers from both frequent prolonged meteorological droughts and scarce long-term ground truth data. The results showed that a rigorous spatially adaptive drought forecasting map can be delineated across LUB via the proposed model. Notably, the LUB was clustered into five drought-hemogenic clusters where multivariate LNN models showed, on average, more than 100% and 40% performance improvement in terms of Nash-Sutcliffe efficiency over the univariate LNN and Seasonal Auto Regressive Integrated Moving Average benchmarks, respectively. This corresponded to an average root mean squared error of 0.25 for the multivariate models, whereas the benchmark errors remained higher, at approximately 0.54 and 0.72, respectively.