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Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol. Model. 160 (3), 265–280. , 2003b. Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network. Water Res. 37 (8), 1749–1758. , 2013. Hazard ratings of pine forests to a pine wilt disease at two spatial scales (individual trees and stands) using self-organizing map and random forest. Ecol. Inform. 13, 40–46. , 2011.
Numerical Ecology. vol. 24. Elsevier, Amsterdam, The Netherlands. , 1995. Improved estimation, using neural networks, of the food consumption of fish populations. Mar. Freshw. Res. 46 (8), 1229–1236. , 1996a. Role of some environmental variables in trout abundance models using neural networks. Aquat. Living Resour. 9 (1), 23–29. , 1996b. Application of neural networks to modelling nonlinear relationships in ecology. Ecol. Model. 90 (1), 39–52. , 2000. Artificial Neuronal Networks: Application to Ecology and Evolution.
GP is an ideal approach for finding solutions where the variables are constantly changing or there is not even an ideal solution. , 2008). , 2006). Olden et al. (2008) summarized the advantages and disadvantages of EC in ecological applications. First of all, EC has the advantage of being able to manage nonlinear data and cope with uneven sampling and small sample sizes. The second advantage is that, theoretically, EC approaches can model complex relationships with a variety of model structures and model-fitting approaches.