Abstract
In this study, precipitation and non-precipitation pattern classification of meteorological radar data is realized with the use of a neuro-fuzzy classifier. Structure expression of meteorological radar data information is analyzed and used to effectively classify precipitation and non-precipitation patterns. Various combinations of input variables for designing pattern classifier are considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data. Pattern classifier is designed by involving essential input variables producing the best performance (classification) of the classifier. The proposed architecture is designed by using FCM-based radial basis function neural network. Two components of classifier such as event classifier part and echo classifier part are designed. In the event classifier part, the pattern classifier identifies precipitation and non-precipitation data. As precipitation data information identified by the event classifier could also involve non-precipitation data information, echo classifier is used to carry out further discrimination processes. The performance of the proposed classifier is evaluated and analyzed in comparison with the existing approaches.
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References
Abbasbandy S, Otadi M, Mosleh M (2008) Numerical solution of a system of fuzzy polynomials by fuzzy neural network. Inf Sci 178(3):1948–1960
Berenguer M, Sempere-Torres D, Corral C, Sánchez-Diezma R (2006) A fuzzy logic technique for identifying nonprecipitating echoes in radar scans. J Atmos Ocean Technol 23(9):1157–1180
Bezdek JC, Rober E, Willian F (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203
Cho YH, Lee KE, Zawadzki I (2006) Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes. J Atmos Ocean Technol 23:1206–1222
Ko JH (2015) A study on pattern classifier based on optimized neuro-fuzzy algorithm for big data information processing: focused on meteorological radar image processing. M.S. thesis, Department of Electric Engineering, The University of Suwon, Korea
Godbole NS, Lamb J (2015) Using data science and big data analytics to make healthcare green. In: 12th international conference and expo on emerging technologies for a smarter world, 2015. IEEE, pp 1–6
Haddad H, Adane A, Sauvageot H, Sadouki L, Naili R (2004) Identification and filtering of rainfall and ground radar echoes using textural features. Int J Remote Sens 25(21):4641–4656
Islam T, Rico-Ramirez MA, Han D, Srivastava PK (2012) Artificial intelligence techniques for clutter identification with polarimetric radar signatures. Atmos Res 109–110:95–113
Kennedy J, Everhart R (1997) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, 1997. IEEE, pp 1942–1948
O’Neil C, Schutt R (2013) Doing data science: straight talk from the frontline. O’Reilly, Newton
Oh SK, Kim WD, Pedrycz W (2011) Polynomial based radial basis function neural networks (P-RBFNNs) realized with the aid of particle swarm optimization. Fuzzy Sets Syst 163(1):54–77
Oh SK, Kim WD, Pedrycz W, Joo SC (2012) Design of k-means clustering-based polynomial radial basis function neural networks(pRBF NNs) realized with the aid of particle swarm optimization and differential evolution. Neurocomputing 78(1):121–132
Priebe T, Markus S (2015) Business information modeling: a methodology for data-intensive projects, data science and big data governance. In: IEEE international conference on big data, 2015. IEEE, pp 2056–2065
Saltz JS (2015) The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness. In: IEEE international conference on big data, 2015. IEEE, pp 2066–2071
Song CS, Kim HK, Oh SK (2014) Design of optimized pattern classifier for discrimination of precipitation and non-precipitation event. J Intell Syst 24(6):586–591
Tang L, Zhang J, Howard K, Lakshmanan V (2014) A physically based precipitation–nonprecipitation radar echo classifier using polarimetric and environmental data in a real-time national system. Weather Forecast 29:1106–1119
Walther A, Schröde M, Fischer J, Bennartz R (2009) Comparison of precipitation in the regional climate model BALTIMOS to radar observations. Theoret Appl Climatol 118(4):627–640
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This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032333).
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Sung-Kwun Oh declares that he has no conflict of interest. Eun-Hu Kim declares that he has no conflict of interest. Jun-Hyun Ko declares that she has no conflict of interest. Kisung Seo declares that he has no conflict of interest.
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Communicated by I. Perfilieva.
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Kim, EH., Ko, JH., Oh, SK. et al. Design of meteorological pattern classification system based on FCM-based radial basis function neural networks using meteorological radar data. Soft Comput 23, 1857–1872 (2019). https://doi.org/10.1007/s00500-018-3539-5
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DOI: https://doi.org/10.1007/s00500-018-3539-5