Application of artificial neural networks for flood forecasting.

Abstract

In hydrology, as in a number of diverse fields, there has been an increasing use of Artificial Neural Networks (ANN) as black-box simplified models. This is mainly justified by their ability to model complex non-linear patterns; in addition they can self-adjust and produce a consistent response when 'trained' using observed outputs. This paper utilises various types of ANNs in an attempt to assess the relative performance of existing models. Ali Efenti, a subcatchment of the river Pinios (Greece), is examined and the results support the hypothesis that ANNs can produce qualitative forecasts. A 7-hour ahead forecast in particular proves to be of fairly high precision, especially when an error prediction technique is introduced to the ANN models.

Visualization-based monitoring in early warning systems with wireless sensor networks

“…technologies [19], [20]. A specific example is the artificial neural network considered for analysis of flood management system in [21] and practically implemented in forecasting the Blue Nile river flows in Sudan [22]. The overview chart for earthquake situational analytics uses data visualization to present the condition across the area around the earthquake zone [23].…”