By Sue Ellen Haupt, Antonello Pasini, Caren Marzban

How can environmental scientists and engineers use the expanding quantity of obtainable info to reinforce our knowing of planet Earth, its platforms and strategies? This e-book describes numerous power ways according to man made intelligence (AI) strategies, together with neural networks, selection bushes, genetic algorithms and fuzzy logic.

Part I features a sequence of tutorials describing the equipment and the $64000 issues in employing them. partially II, many sensible examples illustrate the facility of those options on real environmental problems.

International specialists convey to existence how you can practice AI to difficulties within the environmental sciences. whereas one tradition entwines rules with a thread, one other hyperlinks them with a purple line. hence, a “red thread“ ties the e-book jointly, weaving a tapestry that photos the ‘natural’ data-driven AI tools within the gentle of the extra conventional modeling thoughts, and demonstrating the ability of those data-based methods.

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**Extra resources for Artificial Intelligence Methods in the Environmental Sciences**

**Example text**

The disadvantage is that a training set will end-up being smaller than the whole data set. This disadvantage is not too important in the context of model selection, but it is important if one is using the resampling method for estimating the prediction error. In the model selection context, sampling without replacement has been employed in the literature, but not enough to have earned its own name. One can describe it as hold-out cross-validation with random subsampling (Kohavi 1995). C. Marzban When the sampling is done with replacement, then one can draw a training set as large as the original sample itself.

31) – and there are H + Nout of them. In sum, the network has (Nin + 1)H + (H + 1)Nout parameters. Note the linear growth with Nin . This is how neural nets manage to address the aforementioned curse of dimensionality. For a given number of predictors, they do not have too many parameters, at least compared with polynomial regression. H is the number of hidden nodes, and it plays the role of the order of the polynomial regression. 3 when we squared x and then did regression between y and x 2 . The only difference is that in neural nets circles, one does not use powers of x, but functions that look like a smoothed step function.

They are the parameters of the network – also called weights. 31) is shown in Fig. 6. This network would be referred to as an MLP (or NN) with Nin input nodes, one output node, and two hidden layer of weights (or equivalently, one hidden layer of nodes). The generalizations to multiple output nodes, or multiple hidden layers, are all straightforward. Let us count the parameters: Each line connecting nodes in Fig. 6 is a weight, and there are Nin H + H Nout of them. 31) – and there are H + Nout of them.