ANN requires preliminary learning, during with it modifies itself to reach the desired output. However ANN are very sensitive instruments and require a great deal of data preparation and can be computationally expensive. The important for the decision patterns are subtle or deeply hidden. One of the principal advantages of a neural network is its ability to discover patterns in data which are so obscure as to be imperceptible to human researches and standard statistical methods. The data exhibit significant unpredictable non-linearity.
Neural networks are marvellously adaptable.
The data are chaotic in the mathematical sense. ANN algorithms have been applied to environmental pollution data. The research carried out at EMSA lab. There have been developed hybrid ANN-Geostatistical models for better treatment of trends and to improve spatial predictions. Please, visit related links.