Pine wilt disease (PWD) is one of the most destructive diseases affecting pine trees, and South Korea is one of the most severely damaged countries in the world. Based on the information on PWD occurrence and their environmental characteristics (i.e., geographical, meteorological, and land-use) in South Korea, we evaluated the conditions most conducive for PWD occurrence and developed projection models using machine learning algorithms; random forest (RF) and maximum entropy (Maxent). Our results showed that PWD mainly occurred in areas like highly urbanized area; low elevations, at close proximity to roads. Also, both RF and Maxent models presented high prediction performance for PWD occurrence. Geographical factors (e.g., elevation and distance to roads) were major determinants of PWD occurrence and largely contributed to explaining variability and partial dependence plots of each model. We developed an ensemble model composed of the RF and Maxent models to predict a potential risk map for PWD occurrence on a national scale. In South Korea, most territory was included potential risk of PWD occurrence, and it was predicted to be expanded in the future according to the climate change. The study results showed a high utility for use in surveillance and monitoring of PWD occurrence by inferring the spread pathway or spread direction of PWD based on the potential occurrence map.

松枯病（PWD）是影响松树的最具破坏性的疾病之一，韩国是世界上受灾最严重的国家之一。基于韩国PWD发生及其环境特征（即地理、气象和土地利用）的信息，我们评估了最有利于PWD发生的条件，并使用机器学习算法开发了预测模型；随机森林 (RF) 和最大熵 (Maxent)。我们的研究结果表明，残疾人主要发生在高度城市化地区；低海拔，靠近道路。此外，RF 和 Maxent 模型都对 PWD 的发生具有很高的预测性能。地理因素（例如，海拔和到道路的距离）是 PWD 发生的主要决定因素，并且在很大程度上有助于解释每个模型的可变性和部分依赖图。我们开发了一个由 RF 和 Maxent 模型组成的集成模型，用于预测全国范围内 PWD 发生的潜在风险图。在韩国，大部分领土都包含了PWD发生的潜在风险，预计未来将根据气候变化扩大。研究结果表明，通过基于潜在发生地图推断 PWD 的传播途径或传播方向，在监测和监测 PWD 发生方面具有很高的实用性。在韩国，大部分领土都包含了PWD发生的潜在风险，预计未来将根据气候变化扩大。研究结果表明，通过基于潜在发生地图推断 PWD 的传播途径或传播方向，在监测和监测 PWD 发生方面具有很高的实用性。在韩国，大部分领土都包含了PWD发生的潜在风险，预计未来将根据气候变化扩大。研究结果表明，通过基于潜在发生地图推断 PWD 的传播途径或传播方向，在监测和监测 PWD 发生方面具有很高的实用性。