1. Boosting a fast neural network for supervised land cover classification;Canty;Computer and Geosciences,2009
2. Autoregressive forecast of monthly total ozone concentration: a neurocomputing approach;Chattopadhyay;Computers and Geosciences,2009
3. Chibani, Y., Nemmour, H., 2003. Kalman filtering as a multilayer perceptron training algorithm for detecting changes in remotely sensed imagery. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium IGARSS’03. Toulouse, France, pp. 4101–4103.
4. A comparison of nonlinear regression and neural network models for ground-level ozone forecasting;Cobourn;Journal of Air and Waste Management Association,2000
5. Comparing neural networks and regression models for ozone forecasting;Comrie;Journal of Air and Waste Management Association,1997