Academic Journal of Mathematical Sciences, 2023, 4(1); doi: 10.25236/AJMS.2023.040107.
Yinghao Meng1, Erzhen Lou2, Yufei Zheng3, Bowen Liu4, Xun Zheng1
1College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China
2College of Mathematics and Physics, Wenzhou University, Wenzhou, China
3School of Information Science and Engineering, University of Jinan, Jinan, China
4College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
In this paper, we aim to identify the countries with the most frequent nuclear weapons activity in the last decade and to predict the number of nuclear bombs in the world. First we developed the TOPSIS evaluation model and used the number of nuclear tests and the change in nuclear weapons as two indicators for the model. We eventually concluded that North Korea has been the most active country in developing nuclear weapons in the last decade. We then built a random forest prediction model to predict the number of nuclear-armed states. An LSTM prediction model was then built to predict the number of nuclear weapons in the world and in each country. The predictions gave us: the number of nuclear-armed states in the next 100 years is 8. The number of nuclear weapons per country in 2123 is: China (360), France (258), India (183), Iraq (91), North Korea (11), Pakistan (200), Russia (200), UK (191) and USA (3319). Finally, we conclude with a few recommendations for peace in the human world based on our findings.
TOPSIS, Random Forest, LSTM, Nuclear Weapons
Yinghao Meng, Erzhen Lou, Yufei Zheng, Bowen Liu, Xun Zheng. Prediction modeling of nuclear bomb numbers based on random forests and LSTM. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 1: 44-51. https://doi.org/10.25236/AJMS.2023.040107.
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