کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6963384 | 1452284 | 2015 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predicting fish species richness in estuaries: Which modelling technique to use?
ترجمه فارسی عنوان
پیش بینی غنای گونه های ماهی در استایون ها: کدام مدل سازی برای استفاده؟
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
چکیده انگلیسی
Four different modelling techniques were compared and evaluated: generalized linear models (GLM), generalized additive models (GAM), classification and regression trees (CART) and boosted regression trees (BRT). Each method was used to model fish species richness variation throughout several Portuguese estuarine systems. Model comparisons were based on goodness-of-fit and predictive performance via cross-validation. The relative influence of the most important predictors according to each of the four models was also examined. Fitted BRT, CART, GAM and GLM models accounted for 70.6%, 57.0%, 34.6% and 23.7% of total model deviance, respectively. No single variable was consistently responsible for the larger amount of percentage of relative deviance explained by the models, but several variables were selected by the four models. Nevertheless, their relative importance was highly variable, according to each modelling technique. The tree-based models (CART and BRT) presented lower prediction errors after cross-validation. The limitations and usefulness of each technique are discussed.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 66, April 2015, Pages 17-26
Journal: Environmental Modelling & Software - Volume 66, April 2015, Pages 17-26
نویسندگان
Susana França, Henrique N. Cabral,