By Sue Ellen Haupt, Antonello Pasini, Caren Marzban
How can environmental scientists and engineers use the expanding volume of obtainable facts to reinforce our realizing of planet Earth, its platforms and approaches? This ebook describes a number of power ways in keeping with man made intelligence (AI) recommendations, together with neural networks, selection bushes, genetic algorithms and fuzzy logic.
Part I features a sequence of tutorials describing the equipment and the real issues in using them. partially II, many useful examples illustrate the facility of those suggestions on real environmental problems.
International specialists deliver to lifestyles how one can observe AI to difficulties within the environmental sciences. whereas one tradition entwines principles with a thread, one other hyperlinks them with a pink line. therefore, a “red thread“ ties the ebook jointly, weaving a tapestry that photographs 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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Additional resources for Artificial Intelligence Methods in the Environmental Sciences
After all, the prediction error is a random variable, and therefore, a point estimate of prediction error is subject to variability. In deciding on a good model, it is important to have some sense of the variability of the prediction error. Efron and Tibshirani (1997) report some analytic formulas for computing the standard error of prediction error, but the formulas do not apply to all performance measures. That is not a problem, because one can again rely on resampling techniques to estimate the variability of the prediction error.
Then histograms or boxplots of prediction errors will give us a sense of the variability of the prediction error. One last technical (but important) point remains. Suppose we have completed this type of doublebootstrap and have obtained two boxplots of prediction errors, one from model A, and another for model B. Can we simply compare these boxplots by placing them side-by-side and noting their relative position? For example, if there is significant overlap between the two boxplots, can we conclude that the two models are statistically equivalent?
28), and the apparent error is obtained by training the model one time on the entire data set. 29) to estimate the prediction error. , has the best generalization performance (in AI lingo). Now, note that all of this resampling has gone to produce a single point estimate of the prediction error. Although that may suffice for many applications, it does not when model selection is the task. To properly select the best model, we need an interval estimate of the prediction error. After all, the prediction error is a random variable, and therefore, a point estimate of prediction error is subject to variability.