Applying Artificial Neural Networks in Environmental Prediction Systems

One of the main environmental problem that need efficient software tools is the prediction problem. More concrete, it can mean meteorological prediction, air/soil/water pollution prediction, flood prediction and so on. In the last decade several methods based on artificial intelligence were proposed by taken into account that they can offer more informed methods that use domain specific knowledge and provide solutions faster than the traditional methods, those based on a mathematical formalism. In this paper we present two case studies of applying feedforward artificial neural networks to air pollution forecast and to flood forecast in a hydrographic basin. Some experimental results are also discussed. The neural based prediction can be integrated in a more complex real time monitoring, analysis, and control system for environmental pollution or hydrological processes. Key-Words: Feedforward Artificial Neural Network, Air Pollution Forecast, Flood Forecast, Hydrographic Basin