River flood forecasting with a neural network model

TLDR

A neural network model was developed to analyze and forecast the behavior of the river Tagliamento during heavy rain periods. The model uses distributed rainfall data from multiple mountain‑district gauges and preceding water‑level measurements at the district’s outlet to predict the river level at that section. Model predictions are highly accurate (MSE < 4 %) for a 1‑hour horizon, remain satisfactory up to 5 hours, but accuracy drops beyond that limit, which corresponds to the minimum lag between outlet water level and rainfall.

Abstract

A neural network model was developed to analyze and forecast the behavior of the river Tagliamento, in Italy, during heavy rain periods. The model makes use of distributed rainfall information coming from several rain gauges in the mountain district and predicts the water level of the river at the section closing the mountain district. The water level at the closing section in the hours preceding the event was used to characterize the behavior of the river system subject to the rainfall perturbation. Model predictions are very accurate (i.e., mean square error is less than 4%) when the model is used with a 1‐hour time horizon. Increasing the time horizon, thus making the model suitable for flood forecasting, decreases the accuracy of the model. A limiting time horizon is found corresponding to the minimum time lag between the water level at the closing section and the rainfall, which is characteristic of each flooding event and depends on the rainfall and on the state of saturation of the basin. Performance of the model remains satisfactory up to 5 hours. A model of this type using just rainfall and water level information does not appear to be capable of predicting beyond this time limit.