Bayesian Networks (BNs) are an increasingly popular method for modelling environmental systems. The discretization of continuous variables is often required to use BNs. There are three main methods of discretization; manual, unsupervised, and supervised. Here, we compare and demonstrate each approach with a BN that predicts coastal erosion. Results reveal that supervised discretization methods produced BNs of the highest average predictive skill (73.8%), followed by manual discretization (69.0%) and unsupervised discretization (64.8%). However, each method has specific advantages that may make them more suitable for particular applications. Manual methods can produce physical meaningful BNs, which is favorable in environmental modelling. Supervised methods can autonomously and optimally discretize variables and may be preferred when predictive skill is a modelling priority. Unsupervised methods are computationally simple and versatile. The optimal discretization scheme should consider both the performance and practicality of the scheme.

贝叶斯网络（BN）是一种越来越流行的环境系统建模方法。使用BN通常需要连续变量的离散化。离散化主要有三种方法：手动，无监督和监督。在这里，我们用预测海岸侵蚀的BN来比较和演示每种方法。结果表明，监督离散化方法产生的BN具有最高的平均预测技能（73.8％），其次是手动离散化（69.0％）和无监督离散化（64.8％）。但是，每种方法都有其特定的优点，可能使它们更适合于特定的应用程序。手动方法可以产生有意义的物理BN，这在环境建模中非常有利。有监督的方法可以自主且最佳地离散化变量，并且当预测技能是建模优先级时，可能是首选方法。无监督方法在计算上是简单且通用的。最佳离散化方案应同时考虑该方案的性能和实用性。