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The feasibility of reconstructing total spring precipitation for the South Platte River basin from tree-ring chronologies using artificial neural networks is explored. The use of artificial neural networks allows a comparison of reconstructions resulting from both linear and nonlinear models. Both types of models produced reconstructions that explained more than 40% of the variation in spring precipitation and were well verified with independent data. Although the nonlinear models produced higher R2 values than did the linear model for the calibration period, they performed less well in the independent period. This result and other model evaluation statistics suggest that, in this study, the nonlinear models contain...
Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator – CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with...
The use of advanced modelling methods in ecology expands as ecological data accumulates and increases in complexity. Artificial neural networks (ANN), and in particular, the self-organising map (SOM), has become very popular for analysing particular kinds of ecological datasets. As SOM have become more utilised, it has become increasingly clear that the results of SOM models must be interpreted carefully. SOM have been used in a number of ecological studies to investigate the spatial distribution of species. When using presence–absence data of species distributions at given locations, the input vectors to a SOM are binary and the connection weights after learning are between 0 and 1. Using fuzzy set theory, we...