Rainfall rates derived from “Tipping-Bucket rain gauges” generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional Linear Interpolation Method (LIM) and the Cubic Spline Algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt Observatories, U.K. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash-Sutcliffe Efficiency (NSE), lower Root Mean Square Error (RMSE) and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.
- Water and Environmental Engineering
- Rainfall rate/intensity
- Tipping-Bucket rain gauge