Smoothed Prediction of the Onset of Tree Stem Radius Increase Based on Temperature Patterns

摘要: Possible changes of the growing season trees would have significant consequences on forest production. Predicting onset tree growth basis climate records can be used for estimating magnitude such changes. Conventional methods use cumulative temperature sums. These estimates, however, are quite coarse, and raise questions about making better weather information available. We approach problem predicting with a predictor based combination k-nearest neighbor regressor linear regressor. The inputs weighted sums daily temperatures, where weights determined by subset Bernstein polynomials chosen variable selection methodology. predictions smoothed consecutive days to give more accurate results. compare our proposed solution conventional approach. is found better.