{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:15:27Z","timestamp":1775913327020,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T00:00:00Z","timestamp":1701734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council for Scientific and Technological Development (CNPq)","award":["407250\/2021-2"],"award-info":[{"award-number":["407250\/2021-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lightning Electromagnetic Pulses, or LEMPs, propagate in the Earth\u2013ionosphere waveguide and can be detected remotely by ground-based lightning electric field sensors. LEMPs produced by different types of lightning processes have different signatures. A single thunderstorm can produce thousands of LEMPs, which makes their classification virtually impossible to carry out manually. The lightning classification is important to distinguish the types of thunderstorms and to know their severity. Lightning type is also related to aerosol concentration and can reveal wildfires. Artificial Intelligence (AI) is a good approach to recognizing patterns and dealing with huge datasets. AI is the general denomination for different Machine Learning Algorithms (MLAs) including deep learning and others. The constant improvements in the AI field show us that most of the Lightning Location Systems (LLS) will soon incorporate those techniques to improve their performance in the lightning-type classification task. In this study, we assess the performance of different MLAs, including a SVM (Support Vector Machine), MLP (Multi-Layer Perceptron), FCN (Fully Convolutional Network), and Residual Neural Network (ResNet) in the task of LEMP classification. We also address different aspects of the dataset that can interfere with the classification problem, including data balance, noise level, and LEMP recorded length.<\/jats:p>","DOI":"10.3390\/rs15245635","type":"journal-article","created":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T08:09:17Z","timestamp":1701763757000},"page":"5635","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Performance Analysis of Artificial Intelligence Approaches for LEMP Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0606-2950","authenticated-orcid":false,"given":"Adonis F. R.","family":"Leal","sequence":"first","affiliation":[{"name":"Langmuir Laboratory and Physics Department, New Mexico Institute of Mining and Technology, 801 Leroy Place, Socorro, NM 87801, USA"},{"name":"Graduate Program in Electrical Engineering, Federal University of Para, Belem 66075110, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1610-8986","authenticated-orcid":false,"given":"Gabriel A. V. S.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering, Federal University of Para, Belem 66075110, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8454-0183","authenticated-orcid":false,"given":"Wendler L. N.","family":"Matos","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering, Federal University of Para, Belem 66075110, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rakov, V.A. (2016). Fundamentals of Lightning, Cambridge University Press.","DOI":"10.1017\/CBO9781139680370"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TEMC.2021.3059266","article-title":"Characterization of Lightning Electric Field Waveforms Using a Large Database: 1. Methodology","volume":"63","author":"Leal","year":"2021","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1109\/TEMC.2021.3062172","article-title":"Characterization of Lightning Electric Field Waveforms Using a Large Database: 2. Analysis and Results","volume":"63","author":"Leal","year":"2021","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1029\/2011JD017196","article-title":"New Measurements of Lightning Electric Fields in Florida: Waveform Characteristics, Interaction with the Ionosphere, and Peak Current Estimates","volume":"117","author":"Haddad","year":"2012","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6307","DOI":"10.1029\/JC084iC10p06307","article-title":"Characterization of Lightning Return Stroke Electric and Magnetic Fields from Simultaneous Two-Station Measurements","volume":"84","author":"Lin","year":"1979","journal-title":"J. Geophys. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0169-8095(99)00011-3","article-title":"The Behavior of Total Lightning Activity in Severe Florida Thunderstorms","volume":"51","author":"Williams","year":"1999","journal-title":"Atmos. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106548","DOI":"10.1016\/j.atmosres.2022.106548","article-title":"Nowcasting Extreme Rain and Extreme Wind Speed with Machine Learning Techniques Applied to Different Input Datasets","volume":"282","author":"Chkeir","year":"2023","journal-title":"Atmos. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1029\/2003GL017834","article-title":"Narrow Bipolar Events as Indicators of Thunderstorm Convective Strength","volume":"30","author":"Suszcynsky","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1175\/MWR2915.1","article-title":"Comparison of Narrow Bipolar Events with Ordinary Lightning as Proxies for Severe Convection","volume":"133","author":"Jacobson","year":"2005","journal-title":"Mon. Weather. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1175\/2010MWR3532.1","article-title":"Evolution of Eyewall Convective Events as Indicated by Intracloud and Cloud-to-Ground Lightning Activity during the Rapid Intensification of Hurricanes Rita and Katrina","volume":"139","author":"Fierro","year":"2011","journal-title":"Mon. Weather. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2019GL086825","DOI":"10.1029\/2019GL086825","article-title":"Aerosol Effects on Lightning Characteristics: A Comparison of Polluted and Clean Regimes","volume":"47","author":"Liu","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"ACL 2-1","DOI":"10.1029\/2001JD001142","article-title":"Cloud-to-Ground Lightning Characteristics over Houston, Texas: 1989\u20132000","volume":"107","author":"Steiger","year":"2002","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1029\/2003GL017496","article-title":"Evidence of Thermal and Aerosol Effects on the Cloud-to-Ground Lightning Density and Polarity over Large Urban Areas of Southeastern Brazil","volume":"30","author":"Naccarato","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","first-page":"77","article-title":"Enhanced Positive Cloud-to-Ground Lightning in Thunderstorms Ingesting Smoke from Fires","volume":"282","author":"Lyons","year":"1998","journal-title":"Science (1979)"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1029\/2000GL011656","article-title":"Effect of Pollution from Central American Fires on Cloud-to-ground Lightning in May 1998","volume":"27","author":"Murray","year":"2000","journal-title":"Geophys. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"L03804","DOI":"10.1029\/2005GL024608","article-title":"Cloud-to-Ground Lightning Downwind of the 2002 Hayman Forest Fire in Colorado","volume":"33","author":"Lang","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1029\/2009JA014777","article-title":"Polarity and Energetics of Inner Core Lightning in Three Intense North Atlantic Hurricanes","volume":"115","author":"Thomas","year":"2010","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1029\/1999GL010856","article-title":"A GPS-Based Three-Dimensional Lightning Mapping System: Initial Observations in Central New Mexico","volume":"26","author":"Rison","year":"1999","journal-title":"Geophys. Res. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4183","DOI":"10.1029\/2001JD000502","article-title":"The Los Alamos Sferic Array: A Research Tool for Lightning Investigations","volume":"107","author":"Smith","year":"2002","journal-title":"J. Geophys. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, Q., Ma, Q., Chang, S., He, J., Wang, H., Zhou, X., Xiao, F., and Gao, C. (2020). Classification of VLF\/LF Lightning Signals Using Sensors and Deep Learning Methods. Sensors, 20.","DOI":"10.3390\/s20041030"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e2020GL091148","DOI":"10.1029\/2020GL091148","article-title":"A Machine-Learning Approach to Classify Cloud-to-Ground and Intracloud Lightning","volume":"48","author":"Zhu","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kohlmann, H., Schulz, W., and Pedeboy, S. (2017, January 2\u20136). Evaluation of EUCLID IC\/CG Classification Performance Based on Ground-Truth Data. Proceedings of the 2017 International Symposium on Lightning Protection (XIV SIPDA), Natal, Brazil.","DOI":"10.1109\/SIPDA.2017.8116896"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TEMC.2019.2891898","article-title":"Performance of the European Lightning Detection Network EUCLID in Case of Various Types of Current Pulses From Upward Lightning Measured at the Peissenberg Tower","volume":"62","author":"Paul","year":"2020","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14651","DOI":"10.1002\/2016JD025574","article-title":"A Study of National Lightning Detection Network Responses to Natural Lightning Based on Ground Truth Data Acquired at LOG with Emphasis on Cloud Discharge Activity","volume":"121","author":"Zhu","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9858","DOI":"10.1002\/2017JD027270","article-title":"Evaluation of ENTLN Performance Characteristics Based on the Ground Truth Natural and Rocket-Triggered Lightning Data Acquired in Florida","volume":"122","author":"Zhu","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","unstructured":"Cummins, K., Zhang, D., Quick, M., Garolera, A., and Myers, J. (2014, January 18\u201319). Performance of the U.S. NLDN during the Kansas Windfarm2012 and 2013 Field Programs. Proceedings of the International Lightning Detection Network, Tucson, AZ, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"D05208","DOI":"10.1029\/2006JD007341","article-title":"National Lightning Detection Network (NLDN) Performance in Southern Arizona, Texas, and Oklahoma in 2003\u20132004","volume":"112","author":"Biagi","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_28","unstructured":"Nag, A., Murphy, M.J., Cummins, K.L., Pifer, A.E., and Cramer, J.A. (2014, January 18\u201321). Recent Evolution of the US. National Lightning Detection Network. Proceedings of the 23rd International Lightning Detection Conference & 5th International Lightning Meteorology Conference, Tucson, AZ, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.epsr.2019.04.016","article-title":"Compact Intracloud Discharges: New Classification of Field Waveforms and Identification by Lightning Locating Systems","volume":"173","author":"Leal","year":"2019","journal-title":"Electr. Power Syst. Res."},{"key":"ref_30","unstructured":"Bosacchi, B., Fogel, D.B., and Bezdek, J.C. (2002). Genetic Algorithms and Support Vector Machines for Time Series Classification, SPIE."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/TEMC.2017.2723524","article-title":"A Low-Cost System for Measuring Lightning Electric Field Waveforms, Its Calibration and Application to Remote Measurements of Currents","volume":"60","author":"Leal","year":"2018","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TEMC.2018.2822695","article-title":"Upgrading a Low-Cost System for Measuring Lightning Electric Field Waveforms","volume":"61","author":"Leal","year":"2019","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s10462-018-9614-6","article-title":"Problem Formulations and Solvers in Linear SVM: A Review","volume":"52","author":"Chauhan","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0925-2312(91)90023-5","article-title":"Multilayer Perceptrons for Classification and Regression","volume":"2","author":"Murtagh","year":"1991","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, J., Liu, B., Zhang, H., He, S., and Yang, Q. (2021). Fault Detection Based on Fully Convolutional Networks (FCN). J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9030259"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., and Zhang, L. (2018). A Fully Convolutional Network for Weed Mapping of Unmanned Aerial Vehicle (UAV) Imagery. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196302"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, Q., Xu, J., and Koltun, V. (2017, January 22\u201329). Fast Image Processing with Fully-Convolutional Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.273"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation, Springer.","DOI":"10.1007\/11941439_114"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A Systematic Analysis of Performance Measures for Classification Tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1007\/s10489-021-02635-5","article-title":"Confidence Interval for Micro-Averaged F1 and Macro-Averaged F1 Scores","volume":"52","author":"Takahashi","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_43","unstructured":"M\u00fcller, A., and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists, O\u2032Reilly Media, Inc."},{"key":"ref_44","unstructured":"Raschka, S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Leal, A.F.R., Rakov, V.A., and Rocha, B.R.P. (2017, January 2\u20136). Estimation of Ionospheric Reflection Heights Using CG and IC Lightning Electric Field Waveforms. Proceedings of the 2017 International Symposium on Lightning Protection (XIV SIPDA), Natal, Brazil.","DOI":"10.1109\/SIPDA.2017.8116926"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105426","DOI":"10.1016\/j.jastp.2020.105426","article-title":"Comparison of Ionospheric Reflection Heights for LEMPs Produced by Lightning Return Strokes of Different Polarity","volume":"211","author":"Leal","year":"2020","journal-title":"J. Atmos. Sol. Terr. Phys."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/24\/5635\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:33:39Z","timestamp":1760132019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/24\/5635"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,5]]},"references-count":47,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15245635"],"URL":"https:\/\/doi.org\/10.3390\/rs15245635","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,5]]}}}