Lightning is a natural phenomenon in nature, and its occurrence rules and effects are very complex. The analysis of meteorological elements plays an extremely important guiding role in lightning prediction. Therefore, this paper uses the method of machine learning to carry out early warning analysis on lightning approaching, in order to obtain weather information in advance and take action to avoid danger. In this paper, the analytic hierarchy process (AHP) and the survey method are mainly used to carry out the correlation analysis on the lightning approaching warning. The survey data shows that in this system, the accuracy rate of lightning early warning using decision tree algorithm can reach about 90%, indicating that machine learning has a good effect in lightning approaching early warning.

Asia Khan. Lightning Warning Methods Based on Machine Learning and Single Station Ground Meteorological Elements. Machine Learning Theory and Practice (2021), Vol. 2, Issue 1: 38-46. https://doi.org/10.38007/ML.2021.020105.

[1] Irshad Ullah, Md Nor Ramdon Bin Baharom, Hussein bin Ahmad, Fazli Wahid, Hakim bin Mahmod Luqman, Zainab Zainal, Bhagwan Das: Smart Lightning Detection System for Smart-City Infrastructure Using Artificial Neural Network. Wirel. Pers. Commun. 106(4): 1743-1766 (2019). https://doi.org/10.1007/s11277-018-5383-4

[2] Pierre Kokou, Philip Willemsen, Mounir Lekouara, Madani Arioua, Andreu Mora, Pieter Van den Braembussche, Emanuele Neri, Donny M. A. Aminou: Algorithmic Chain for Lightning Detection and False Event Filtering Based on the MTG Lightning Imager. IEEE Trans. Geosci. Remote. Sens. 56(9): 5115-5124 (2018). https://doi.org/10.1109/TGRS.2018.2808965

[3] Anastasios Petropoulos, Vasilis Siakoulis, Evangelos Stavroulakis: Towards an Early Warning System for Sovereign Defaults Leveraging on Machine Learning Methodologies. Intell. Syst. Account. Finance Manag. 29(2): 118-129 (2022). https://doi.org/10.1002/isaf.1516

[4] Silvester Dian Handy Permana, Gusti Saputra, Budi Arifitama, Yaddarabullah, Wahyu Caesarendra, Robbi Rahim: Classification of bird sounds as an early warning method of forest fires using Convolutional Neural Network (CNN) algorithm. J. King Saud Univ. Comput. Inf. Sci. 34(7): 4345-4357 (2022). https://doi.org/10.1016/j.jksuci.2021.04.013

[5] Gemma Cremen, Carmine Galasso: A Decision-Making Methodology for Risk-Informed Earthquake Early Warning. Comput. Aided Civ. Infrastructure Eng. 36(6): 747-761 (2021). https://doi.org/10.1111/mice.12670

[6] Maman Abdurohman, Aji Gautama Putrada, Mustafa Mat Deris: A Robust Internet of Things-Based Aquarium Control System Using Decision Tree Regression Algorithm. IEEE Access 10: 56937-56951 (2022). https://doi.org/10.1109/ACCESS.2022.3177225

[7] Hadeer Mahmoud, Mostafa Thabet, Mohamed Helmy Khafagy, Fatma A. Omara: Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm. IEEE Access 10: 36140-36151 (2022). https://doi.org/10.1109/ACCESS.2022.3163273

[8] Marziye Narangifard, Hooman Tahayori, Hamid Reza Ghaedsharaf, Mehrdad Tirandazian: Early Diagnosis of Coronary Artery Disease by SVM, Decision Tree Algorithms and Ensemble Methods. Int. J. Medical Eng. Informatics 14(4): 295-305 (2022). https://doi.org/10.1504/IJMEI.2022.123921

[9] Chandrashekhar Azad, Bharat Bhushan, Rohit Sharma, Achyut Shankar, Krishna Kant Singh, Aditya Khamparia: Prediction Model Using SMOTE, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus. Multim. Syst. 28(4): 1289-1307 (2022). https://doi.org/10.1007/s00530-021-00817-2

[10] Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae-Min Lee, Dong-Seong Kim: Efficient Classification of Enciphered SCADA Network Traffic in Smart Factory Using Decision Tree Algorithm. IEEE Access 9: 154892-154901 (2021). https://doi.org/10.1109/ACCESS.2021.3127560

[11] Leidiane C. M. M. Fontoura, Hertz Wilton De Castro Lins, Arthur S. Bertuleza, Adaildo G. D'Assunção, Alfredo Gomes Neto: Synthesis of Multiband Frequency Selective Surfaces Using Machine Learning With the Decision Tree Algorithm. IEEE Access 9: 85785-85794 (2021). https://doi.org/10.1109/ACCESS.2021.3086777

[12] Kumpol Saengtabtim, Natt Leelawat, Jing Tang, Wanit Treeranurat, Narunporn Wisittiwong, Anawat Suppasri, Kwanchai Pakoksung, Fumihiko Imamura, Noriyuki Takahashi, Ingrid Charvet: Predictive Analysis of the Building Damage From the 2011 Great East Japan Tsunami Using Decision Tree Classification Related Algorithms. IEEE Access 9: 31065-31077 (2021). https://doi.org/10.1109/ACCESS.2021.3060114

[13] Ferdinand Bollwein, Stephan Westphal: A Branch & Bound Algorithm to Determine Optimal Bivariate Splits for Oblique Decision Tree Induction. Appl. Intell. 51(10): 7552-7572 (2021). https://doi.org/10.1007/s10489-021-02281-x

[14] Muhamad Hasbullah Mohd Razali, Rizauddin Saian, Yap Bee Wah, Ku Ruhana Ku-Mahamud: An Improved ACO-Based Decision Tree Algorithm for Imbalanced Datasets. Int. J. Math. Model. Numer. Optimisation 11(4): 412-427 (2021). https://doi.org/10.1504/IJMMNO.2021.10041068

[15] Firoozeh Karimi, Selima Sultana, Ali Shirzadi Babakan, Shan Suthaharan: Urban Expansion Modeling Using an Enhanced Decision Tree Algorithm. GeoInformatica 25(4): 715-731 (2021). https://doi.org/10.1007/s10707-019-00377-8

[16] Matheus Guedes Vilas Boas, Haroldo Gambini Santos, Luiz Henrique de Campos Merschmann, Greet Vanden Berghe: Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches. Int. Trans. Oper. Res. 28(5): 2759-2781 (2021). https://doi.org/10.1111/itor.12724

[17] Rajendra Mahla, Baseem Khan, Om Prakash Mahela, Anup Singh: Recognition of Complex and Multiple Power Quality Disturbances Using Wavelet Packet-Based Fast Kurtogram and Ruled Decision Tree Algorithm. Int. J. Model. Simul. Sci. Comput. 12(5): 2150032:1-2150032:23 (2021). https://doi.org/10.1142/S179396232150032X

[18] Vinay Arora, Rohan Singh Leekha, Inderveer Chana: An Efficacy of Spectral Features with Boosted Decision Tree Algorithm for Automatic Heart Sound Classification. J. Medical Imaging Health Informatics 11(2): 513-528 (2021). https://doi.org/10.1166/jmihi.2021.3287