Runoff Prediction Models(RPM) are those models which predict the runoff or flood peak of a watershed or input runoff to a dam etc. Generally, such models require climatic parameters, geomorphology, soil characteristics, and land cover as input against which a runoff model can predict the monthly, weekly, daily, or even hourly runoff. These models can be spatially distributed or lumped, temporally long or short, data-driven or conceptual.
In recent years due to the massive development in data-driven and smart concepts like artificial neural networks(ANN), decision trees, and evolutionary algorithms, application of such techniques are now common to develop Runoff Prediction Models(RPM). Among data-driven techniques, ANN is the most popular, followed by evolutional algorithms. But compared to the standalone application of neural networks, hybrid models where ANN with conceptual models like HyMOD or HEC, etc. is reported to be more successful in the prediction of runoff.
The seven most recent Runoff Prediction Models(RPM) are selected based on their accuracy, reliability, ease of use, and recentness.