Abstract
The paper explores the possibility of forecasting such dangerous meteorological phenomena as a thunderstorm by applying five types of neural network to the output data of a hydrodynamic model that simulates dynamic and microphysical processes in convective clouds. The ideas and the result delivered in [1] are developed and supplemented by the classification error calculations and by consideration of radial basic and probabilistic neural networks. The results show that forecast accuracy of all five networks reaches values of 90%. However, the radial basis function has the advantages of the highest accuracy along with the smallest classification error. Its simple structure and short training time make this type of neuralnetwork the best one in view of accuracy versus productivity relation.
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References
Stankova, E.N., Tokareva, I.O., Dyachenko, N.V.: On the effectiveness of using various machine learning methods for forecasting dangerous convective phenomena. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 82–93. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58817-5_7
Schultz, M.G., et al.: Can deep learning beat numerical weather prediction? Phil. Trans. R. Soc. A 379, 20200097 (2021). https://doi.org/10.1098/rsta.2020.0097
Scher, S., Messori, G.: Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground. Geosci. Model Dev. 12, 2797–2809 (2019). https://doi.org/10.5194/gmd-12-2797-2019
Kugliowski, R.J., Barros, A.P.: Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather Forecast. 13(4), 1194–1204 (1998)
Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. 13(8), 1413–1425 (2009)
Unwetterklimatologie: Starkregen. https://www.dwd.de/DE/leistungen/unwetterklima/starkregen/starkregen.html. Accessed 30 April 2020
Luk, K.C., Ball, J.E., Sharma, A.: An application of artificial neural networks for rainfall forecasting. Math. Comput. Model. 33(6–7), 683–693 (2001). https://doi.org/10.1016/S0895-7177(00)00272-7
Tao, Y., Gao, X., Ihler, A., Sorooshian, S.: Deep neural networks for precipitation estimation from remotely sensed information. In: Proceedings IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 1349–1355. IEEE (2016)
Tao, Y., Gao, X., Ihler, A., Sorooshian, S., Hsu, K.: Precipitation identification with bispectral satellite information using deep learning approaches. J. Hydrometeor. 18, 1271–1283 (2017)
Hall, T., Brooks, H.E., Doswell, C.A., III.: Precipitation forecasting using a neural network. Weather Forecast. 14(3), 338–345 (1999)
Culclasure, Andrew, Using Neural Networks to Provide Local Weather Forecasts” (2013). Electronic Theses and Dissertations. 32. https://digitalcommons.georgiasouthern.edu/etd/32
Santhanam, T., Subhajini, A.C.: An efficient weather forecasting system using radial basis function neural network. J. Comput. Sci. 7(7), 962–966 (2011)
Marzban, C., Stumpf, G.J.: A neural network for tornado prediction based on Doppler radar-derived attributes. J. Appl. Meteorol. 35(5), 617–626 (1996)
Baik, J.-J., Paek, J.-S.: A Neural Network Model for predicting typhoon intensity. J. Meteor. Soc. Japan. (2000). https://doi.org/10.2151/jmsj1965.78.6857
Ruettgers, M., Lee, S., Jeon, S., You, D.: Prediction of a typhoon track using a generative adversarial network and satellite images. Sci. Rep. 9, 6057 (2019). https://doi.org/10.1038/s41598-019-42339-y
Stankova, E.N., Grechko, I.A., Kachalkina, Y.N., Khvatkov, E.V.: Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10408, pp. 495–504. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62404-4_37
Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Korkhov, V.V., Shorov, A.V.: OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds. Int. J. Bus. Intell. Data Min. 14(1/2), 254 (2019). https://doi.org/10.1504/IJBIDM.2019.096793
Stankova, E.N., Khvatkov, E.V.: Using boosted k-nearest neighbour algorithm for numerical forecasting of dangerous convective phenomena. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11622, pp. 802–811. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24305-0_61
Raba, N.O., Stankova, E.N.: Research of influence of compensating descending flow on cloud's life cycle by means of 1.5-dimensional model with 2 cylinders. In: Proceedings of MGO, vol. 559, pp. 192–209 (2009). (in Russian)
Raba, N., Stankova, E.: On the possibilities of multi-core processor use for real-time forecast of dangerous convective phenomena. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010. LNCS, vol. 6017, pp. 130–138. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12165-4_11
Raba, N.O., Stankova, E.N.: On the problem of numerical modeling of dangerous convective phenomena: possibilities of real-time forecast with the help of multi-core processors. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011. LNCS, vol. 6786, pp. 633–642. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21934-4_51
Raba, N.O., Stankova, E.N.: On the effectiveness of using the GPU for numerical solution of stochastic collection equation. In: Murgante, B., et al. (eds.) ICCSA 2013. LNCS, vol. 7975, pp. 248–258. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39640-3_18
Dudarov, S.P., Diev, A.N.: Neural network modeling based on perceptron complexes withsmall training data sets. Math. Meth. Eng. Technol. 26, 114–116 (2013). (in Russian)
Dudarov, S.P., Diev, A.N., Fedosova, N.A., Koltsova, E.M.: Simulation of properties of composite materials reinforced by carbon nanotubes using perceptron complexes. Comput. Res. Model. 7(2), 253–262 (2015). https://doi.org/10.20537/2076-7633-2015-7-2-253-262
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Stankova, E., Tokareva, I.O., Dyachenko, N.V. (2021). On the Possibility of Using Neural Networks for the Thunderstorm Forecasting. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12956. Springer, Cham. https://doi.org/10.1007/978-3-030-87010-2_25
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