Intelligent algorithm for predicting light radiation damage areas and inverting source parameters in nuclear explosion
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摘要: 光辐射是核爆炸能量耗散的主要形式，对生态环境和人类社会具有深远影响。研究其特性、传播规律和能量分布，可为核爆炸毁伤效应评估与防护提供重要依据。引入Kolmogorov-Arnold网络（KAN），构建了可解释的核爆炸光辐射损伤面积预测模型，同时采用多种优化算法反演出核爆炸的源项参数。首先，基于核爆炸火球理论构建光辐射模型，结合ArcGIS Pro软件实现真实地图下光辐射的热能分布，并根据生物的烧伤伤情分级标准生成爆炸当量、爆炸高度与损伤面积之间的数据集。其次，利用KAN网络进行数据集预测，凭借其独特的可解释性优势获得显式预测公式。再次，采用门控循环单元、极限学习机和随机森林等8种算法对比预测结果，评估KAN的性能。最后，构建核爆炸光辐射模型损失函数，通过多种优化算法获得爆炸当量、爆炸高度和爆心位置等源项信息。本研究实现了对核爆炸光辐射损伤效应的快速预测和精准反演，有助于提高应急响应效率并辅助防护决策。
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关键词:
- 光辐射 /
- ArcGIS Pro /
- Kolmogorov-Arnold网络 /
- 参数反演 /
- 机器学习
Abstract:Background Light radiation, the primary mode of energy dissipation in nuclear explosions, profoundly impacts both the ecological environment and human society. A thorough understanding of its characteristics, propagation dynamics, and energy distribution is therefore essential for evaluating and protecting against nuclear explosion damage effects.Purpose This study introduces the Kolmogorov-Arnold network (KAN) to construct an interpretable model for predicting the area of light radiation damage. The model utilizes multiple optimization algorithms to invert key source term parameters, namely the explosion yield, height of explosion, and explosion location.Methods Based on the theory of nuclear explosion fireballs, a light radiation model was developed and integrated with ArcGIS Pro software to visualize thermal energy distribution on real-world maps. A dataset correlating explosion yield and height with damage area was then generated, quantified according to established standards for biological burn injuries. The KAN was trained on this dataset, leveraging its unique advantage of providing explicit, interpretable formulas for prediction. To validate its efficacy, the KAN's performance was benchmarked against eight other algorithms, including Gated Recurrent Unit (GRU), Extreme Learning Machine (ELM), and Random Forest (RF). A loss function was constructed for the radiation model to facilitate the inversion of source term parameters via multiple optimization algorithms.Results The results demonstrate that the KAN model achieves high prediction accuracy while yielding an interpretable formula for the damage area. Furthermore, both the genetic algorithm and the Hippopotamus optimization algorithm successfully inverted the nuclear source term parameters with high fidelity.Conclusions This methodology facilitates both the rapid prediction of damage effects and the accurate inversion of source parameters, thereby enhancing emergency response efficiency and aiding in strategic protective decision-making.-
Key words:
- light radiation /
- ArcGIS Pro /
- Kolmogorov-Arnold network /
- parameter inversion /
- machine learning
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图 1 光辐射损伤面积预测与反演结构图
Figure 1. Diagram of light radiation damage area prediction and parameter inversion
图 2 真实地图核爆炸光辐射的热能分布
Figure 2. Thermal energy distribution of light radiation from nuclear explosion on real map
图 3 核爆炸光辐射在不同区域的热能分布
Figure 3. Thermal energy distribution of nuclear explosion light radiation in different regions
图 4 不同参数的核爆炸光辐射的热能分布等高线图
Figure 4. Contour map of thermal energy distribution of nuclear explosion light radiation with different parameter
图 6 不同机器学习模型对轻度损伤面积分布的回归预测结果
Figure 6. Regression prediction results of different machine learning models for mild burn area distribution
图 7 不同机器学习模型预测值与真实值对比
Figure 7. Comparison between predicted value and actual value of machine learning models
图 8 不同机器学习模型的预测性能评估
Figure 8. Evaluation of predictive performance for machine learning models
图 9 采样点分布和不同优化算法的适应度曲线
Figure 9. Sampling point distribution and optimization algorithm fitness curves
表 1 不同机器学习模型对轻度损伤面积分布预测指标
Table 1. Performance metrics of machine learning models for mild burn area distribution prediction
prediction algorithm MAE MSE RMSE MAPE KAN 0.078509 0.020217 0.14219 0.0011629 GRU 0.094142 0.017032 0.13051 0.0016689 SVM 0.080676 0.0084754 0.092062 0.0108 ELM 0.032639 0.0040316 0.063495 0.00024225 LSTM 0.20743 0.090121 0.3002 0.33317 BP 0.01153 0.00042322 0.020572 2.3645E-06 RBFNN 0.21585 0.084455 0.29061 0.0046114 CNN 0.2678 0.14225 0.37716 0.047087 RF 0.24701 0.1031 0.32109 0.016381 
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表 2 不同优化算法反演的爆炸源项参数
Table 2. Inverted explosion source parameters by different optimization algorithms
optimization
algorithmsnuclear explosion
yield Q/ktnuclear explosion
height z0/kmx-axis coordinate of
explosion point/kmy-axis coordinate of
explosion point/kmReal 30 1.2 2.0396 1.7349 AO 36.5698 1.4291 2.0681 1.8919 BWO 25.6759 1.05075 2.02041 1.74864 HO 30.7551 1.2296 2.0309 1.7362 TTHHO 32.691 1.30716 2.01174 1.72705 GA 30.50 1.22 2.04 1.74
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