Neural network solution of inverse parameters used in the sensitivity-calibration analyses of the SWMM model simulations
The use of the Storm Water Management Model (SWMM) to simulate Runoff-Transport phenomenon necessitates the proper calibration of the different parameters involved in the process and the effect of these parameters on the routed hydrograph. A detailed sensitivity analysis is needed to evaluate the main parameters of the Runoff-Extended Transport Blocks to establish the most sensitive parameters affecting the Runoff-Extended Transport Simulation. This type of analysis requires tedious trial and error solutions to reach the objective calibration. Artificial Neural Networks (ANN) are known to have the capability of solving the inverse of many problems. The objective of this study is to examine the potential use of ANN to solve the inverse parameters needed for the calibration problem. A hypothetical test area composed of Runoff-Extended Transport Blocks was used to simulate various routed hydrographs based on changing the hydrologichydraulics parameters within its physical engineering ranges. The results show that ANN is a powerful instrument for directly predicting the most sensitive parameters to produce comparable routed hydrograph to the measured ones.
sensitivity-calibration analyses storm water management model storm water management model (swmm) artificial neural networks general regression neural network pcswmm98