CAJ | 학술논문

利用逻辑回归算法和决策树C5.0算法分别获取溢油扩散的转换规则，并构建了基于逻辑回归的CA模型和决策树CA模型。这两个模型仅需要设置起始影像、影响因子和权重等少数的变量，便可以方便地模拟出溢油的动态变化情况。把逻辑回归CA模型和决策树CA模型应用到DeepSpill项目的海上溢油模拟实验，结果表明逻辑回归CA模型的模拟总精度达到96.4%，Kappa系数达0.893，而决策树CA模型的模拟结果更为理想，其精度和kappa系数分别提高了0.2%和0.006。利用元胞自动机能够很好地模拟并预测出海上溢油的动态变化，可以满足对溢油快速响应的要求。
이용라집회귀산법화결책수C5.0산법분별획취일유확산적전환규칙，병구건료기우라집회귀적CA모형화결책수CA모형。저량개모형부수요설치기시영상、영향인자화권중등소수적변량，편가이방편지모의출일유적동태변화정황。파라집회귀CA모형화결책수CA모형응용도DeepSpill항목적해상일유모의실험，결과표명라집회귀CA모형적모의총정도체도96.4%，Kappa계수체0.893，이결책수CA모형적모의결과경위이상，기정도화kappa계수분별제고료0.2%화0.006。이용원포자동궤능구흔호지모의병예측출해상일유적동태변화，가이만족대일유쾌속향응적요구。
Cellular automata (CA) is an effective tool for simulating geographical process. In this paper, logistic regression and decision tree algorithm (C5.0) are introduced to obtain transition rules, which are used to build logistic regression CA model and decision-tree CA model. These two models are very convenient because they only need a few variables, such as starting image, impact factors and weights. And the simulation results of oil spill can be obtained. The logistic regression CA model and decision-tree CA model are applied to simulate the movement of oil spill in Deep Spill projects. Experiment re-sults showed that the overall accuracy and Kappa coefficient of simulation results in logistic regression CA were 96.4%and 0.893.Better results could be obtained using decision-tree CA model. Its overall accuracy and kappa coefficients increased by 0.2%and 0.006. Our experiment results showed that the CA models could simulate the dynamic changes of the oil spill and meet the requirements for rapid response of governments.