Next, using the reference model MODEL3 settings, we limited the time or space of the training data to determine the performance in specific conditions. The accuracy of these spatially restricted models was generally higher than the reference model MODEL3 even though the numbers of training images was almost the same. Globally, there are several different climatic zones with different interannual trends.
Our results may suggest that the DNN is able to capture climatic trends more successfully when the target area is restricted to a single climatic zone.
Among climatic regions, Amazon tropical rain forests shows particularly high accuracy, whereas Siberia boreal forests has relatively low accuracy. This may be caused by the heterogeneity within the climatic regions. When interannual trends in temperature are homogeneous within the climatic region, we expect higher accuracy. This is understandable from the viewpoints of artificial intelligence and computer vision. For example, to construct a model of human faces, numerous training images are required because human faces are heterogeneous. On the other hand, a homogeneous object such as the trade mark of Coca Cola can be identified relatively easily with a few training images with sample size amplification by modifying colors and shapes.
This bias may make predictions easier. Another reason is the data quality; we assume that the quality of the data is improving gradually in the temporal range and this makes predictions more reliable. In this study, we showed that, without physics-based mechanisms, a top-down forecast model can successfully predict rise or fall in temperature in a decadal timescale. Although this study has limitations in predictability because only two classes RISE or FALL are used for forecast, this top-down approach can be a meaningful measure for climate change studies. We suggest that this top-down approach should be used together with physics-based bottom-up approaches because these approaches can work in a complementary manner.
Different color schemes lead to some differences in the model performance Figure 4. We noticed that the rainbow color scheme see Figures 2I—L gives more information to human observers but this may not be the case for an artificial intelligence system constructed using a DNN model.
Original Research ARTICLE
When number of training images is small, the performance of the default color scheme heat. With large numbers of training images, the performance of heat. These findings may be insightful for various studies on artificial intelligence.
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Figure 4. Comparison of accuracy for different colors.
Note that both horizontal and vertical axes are logarithmic axes. This study can be enhanced with several modifications. However, other networks such as AlexNet Krizhevsky et al. There are advantages and disadvantages for this study. Since this is a top-down approach, it is difficult to measure whether our approach would perform well under novel conditions. This limitation is universal for phenomenological forecasting. Therefore, we suggest that climatic forecasting should integrate both the top-down approach such as this study and the bottom-up approach based on physics. This study uncovers several insightful research topics.
For example, the models constructed in this study can be used to forecast future trends in temperature. Moreover, along with the mean monthly temperature, other meteorological datasets such as maximum and minimum temperatures and precipitation can be used to generate images for DNN training. This may further improve the model performance.
We also suggest using temperature data from simulation studies such as ESM.
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By doing this, we may be able to show whether climate simulations generate interannual trends similar to observed data. Overall, because climate change is a very important problem, many different approaches should be used to address it. We hope that our DNN-based, top-down approach can be one such novel approach. TI contributed image processing, coding DNN, and study design.
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YO contributed executing experiments. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Collins, W.
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The community climate system model version 3 CCSM3. Gentine, P. Could machine learning break the convection parameterization deadlock? Harris, I. Intergovernmental Panel on Climate Change Stocker, D. Qin, G. Plattner, M. Tignor, S. Allen, J. Dreiseitl and L. Ohno-Machado, Risk stratification in heart failure using artificial neural networks. AMIA Symp. Israel-Truijillo, M. Juan and G. Gomez, Choosing variables with a genetic algorithm for econometric models based on neural networks learning and adaptation.
Di Caro, G. Dorigo, AntNet: Distributed stigmergetic control for communications networks. Maniezzo and A. Colorni, The ant system: An autocatalytic optimizing process. Dorigo, M. Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Man Cybern. Part B: Cybern.
Adaptation in Natural and Artificial System. Huan, L. Setiono, Dimensionality reduction via discretization. Knowledge-Based Syst. Alidaee, R. Dorsey and J. Johnson, Global optimization for artificial neural networks: A tabu search application. Hoos, Contrary to this belief, there is also a theory that all prices change randomly and it is absolutely impossible to forecast the outcome. Provided that you have no intention to use historical data for analysis, the only strategy which seems to be possible is to sell short and hold. However, a large amount of research shows us that it is possible to make more money if you use different analysis tools.
That is what I would like to investigate in this article. At the present time, it is almost impossible to imagine trading without algorithms. It is assumed to be way better than placing all of your orders manually.