A comparative analysis of numerical, neural network, and hybrid modelling techniques for simulating karst spring discharge based on long-term hydrological records.

The research was carried out at the Manorhamilton karst spring in County Leitrim, northwest Ireland, a representative site of the region’s Carboniferous limestone terrain, notable for its complex subsurface flow networks and rapid hydrological dynamics. The study sought to simulate spring discharge using five years of hydrological observations and to evaluate ten distinct modeling methods. These included a physically based numerical pipe network model built in InfoWorks ICM 2025.1, three neural network (NN) models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Nonlinear Autoregressive with Exogenous Inputs (NARX) and six hybrid numerical–NN configurations. Two hybridization strategies were tested: Residual Error Correction (REC) and Sequential Combination (SC). Results revealed that all NN models surpassed the standalone numerical model in reproducing karst spring discharge time series. Among the hybrids, the LSTM+PN+SC model achieved the highest accuracy, stability, and generalization across various flow regimes. These outcomes underscore the advantages of integrating physical process knowledge with deep learning approaches for modelling intricate karst hydrological systems. The study also outlines the strengths and limitations of applying different NN architectures and hybrid methods for groundwater management and prediction in comparable Irish karst settings.