Hydrological Forecasting Using Neural Networks

Abstract

Operational planning of water resources systems like reservoirs and power plants calls for real-time or on-line forecasting of runoff and river stage. Most of the real-time forecasting models used in the past are of the distributed type, where the forecasts are made at several locations within a catchment area. In situations where the information is needed only at specific sites in a river basin, and needs to be more accurate, the time and effort required in developing and implementing such complicated models may not be justified. Simpler neural network (NN) forecasts may therefore seem attractive as an alternative. The present study demonstrates the application of NNs to real-time forecasting of hourly flood runoff and daily river stage, as well as to the prediction of rainfall sufficiency for India. The study showed the capability of NNs in all of these applications. In many situations they performed better than the statistical models.