Multifeature Fusion-Based Thunderstorm Prediction System With Switchable Patterns

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dc.authoridyang, xu/0000-0002-6877-5641
dc.authorwosidzhao, wenqing/KEZ-9488-2024
dc.authorwosidLiu, Zhe/KEJ-5299-2024
dc.contributor.authorYang, Xu
dc.contributor.authorXing, Hongyan
dc.contributor.authorJi, Xinyuan
dc.contributor.authorZhao, Di
dc.contributor.authorSu, Xin
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:39:11Z
dc.date.available2024-05-19T14:39:11Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAtmospheric electric field signal (AEFS) features can be characterized by their average value (AV), standard deviation (SD), and entropy value (EV). How to mine and fully utilize AEFS features to ensure reliable and efficient thunderstorm detection has not been considered so far. In this article, based on the stacked autoencoder (SAE) and extreme gradient boosting (XGBoost) model, extracted deep-seated features of AEFS are used to obtain its predicted value (PV). It fuses three regular features plus one PV feature and proposes a thunderstorm moving path (TMP) prediction system with switchable patterns among the applied three AEFS prediction models based on the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). This fully considers that a single model is difficult to meet AEFS predictions with different weather attributes. Specifically, AEF data measured by a self-made AEF apparatus are adopted to determine feature values (FVs). According to FV intervals (FVIs) in sunny and thunderstorm weathers, the proportion of each feature satisfying FVIs is taken as the weighting factors of corresponding feature terms. A switchable pattern function with different switching conditions is formed by combining weightings and feature variables. Optimal AEFS prediction models are fixed under the same switching condition and applied to corresponding patterns. Empirical results confirm that the proposed system effectively predicts TMPs, with an average determination coefficient of 95.58%. This is the first study to design switchable patterns to detect thunderstorms from a new perspective of multiple AEFS feature fusion, which provides promising solutions to the refinement and intelligent prediction of thunderstorms.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62171228]; National Key Research and Development Program of China [2021YFE0105500]; Program of China Scholarship Council [202209040027]en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 62171228, in part by the National Key Research and Development Program of China under Grant 2021YFE0105500, and in part by the Program of China Scholarship Council under Grant 202209040027.en_US
dc.identifier.doi10.1109/JSEN.2023.3291397
dc.identifier.endpage18476en_US
dc.identifier.issn1530-437X
dc.identifier.issn1558-1748
dc.identifier.issue16en_US
dc.identifier.startpage18461en_US
dc.identifier.urihttps://doi.org10.1109/JSEN.2023.3291397
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4720
dc.identifier.volume23en_US
dc.identifier.wosWOS:001049997900063en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Sensors Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAtmospheric Electric Field (Aef)en_US
dc.subjectFeatureen_US
dc.subjectPredictionen_US
dc.subjectThunderstormen_US
dc.titleMultifeature Fusion-Based Thunderstorm Prediction System With Switchable Patternsen_US
dc.typeArticleen_US

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