Artificial Neural Network Modeling of the Rainfall‐Runoff Process

TLDR

ANNs are flexible mathematical structures capable of capturing complex nonlinear relationships, making them useful for problems where physical equations are inadequate, though they lack physically realistic components and thus cannot replace conceptual watershed models. This study introduces LLSSIM, a method for identifying the structure and parameters of three‑layer feed‑forward ANN models, to simulate nonlinear hydrologic behavior of watersheds. LLSSIM applies linear least‑squares simplex optimization to determine the architecture and weights of the ANN, enabling efficient model identification. The ANN approach accurately reproduces the rainfall‑runoff relationship of the Leaf River basin, outperforming ARMAX and SAC‑SMA models, and serves as a viable alternative for input‑output simulation and forecasting when internal watershed structure is not required.

Abstract

An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been found useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using physical equations. This study presents a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three‐layer feed forward ANN models and demonstrates the potential of such models for simulating the nonlinear hydrologic behavior of watersheds. The nonlinear ANN model approach is shown to provide a better representation of the rainfall‐runoff relationship of the medium‐size Leaf River basin near Collins, Mississippi, than the linear ARMAX (autoregressive moving average with exogenous inputs) time series approach or the conceptual SAC‐SMA (Sacramento soil moisture accounting) model. Because the ANN approach presented here does not provide models that have physically realistic components and parameters, it is by no means a substitute for conceptual watershed modeling. However, the ANN approach does provide a viable and effective alternative to the ARMAX time series approach for developing input‐output simulation and forecasting models in situations that do not require modeling of the internal structure of the watershed.