Spiking modular neural networks: A neural network modeling approach for hydrological processes

Artificial Neural Networks (ANNs) have been widely used for modeling hydrological processes that are embedded with high nonlinearity in both spatial and temporal scales. The input‐output functional relationship does not remain the same over the entire modeling domain, varying at different spatial and temporal scales. In this study, a novel neural network model called the spiking modular neural networks (SMNNs) is proposed. An SMNN consists of …