Determining forest volume increment, the potential of wood production in natural forests, is a complex issue but is of fundamental importance to sustainable forest management. Determining potential volume increment through growth and yield models is necessary for proper management and future prediction of forest characteristics (diameter, height, volume, etc.). Various methods have been used to determine the productive capacity and amount of acceptable harvest in a forest, and each has advantages and disadvantages. One of these methods involves the artificial neural network techniques, which can be effective in natural resource management due to its flexibility and potentially high accuracy in prediction. This research was conducted in the Ramsar forests of the Mazandaran Province of Iran. Volume increment was estimated using both an artificial neural network and regression methods, and these were directly compared with the actual increment of 20 one-hectare permanent sample plots. A sensitivity analysis for inputs was employed to determine which had the most effect in predicting increment. The actual average annual volume increment of beech was 4.52 m3ha−1 yr−1, the increment was predicted to be 4.35 and 4.02 m3ha−1 yr−1 through the best models developed using an artificial neural network and using regression, respectively. The results showed that an estimate of increment can be predicted relatively well using the artificial neural network method, and that the artificial neural network method is able to estimate the increment with higher accuracy than traditional regression models. The sensitivity analysis showed that the standing volume at the beginning of the measurement period and the diameter of trees had the greatest impact on the variation of volume increment.

中文翻译：

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天然林中东方山毛榉（Fagus orientalis Lipsky）体积增量的十年估算：人工神经网络模型、多元线性回归和实际增量的比较

确定森林体积增量，即天然林中木材生产的潜力，是一个复杂的问题，但对可持续森林管理至关重要。通过生长和产量模型确定潜在的体积增量对于正确管理和未来预测森林特征（直径、高度、体积等）是必要的。已经使用了各种方法来确定森林中可接受的采伐量和生产能力，每种方法都有优点和缺点。其中一种方法涉及人工神经网络技术，由于其灵活性和潜在的高精度预测，该技术可以有效地用于自然资源管理。这项研究是在伊朗马赞达兰省的拉姆萨尔森林中进行的。使用人工神经网络和回归方法估计体积增量，并将这些方法与 20 个 1 公顷永久样地的实际增量进行直接比较。采用对输入的敏感性分析来确定哪个对预测增量的影响最大。山毛榉的实际年均体积增量为 4.52 m3ha-1 yr-1，通过使用人工神经网络和回归开发的最佳模型分别预测增量为 4.35 和 4.02 m3ha-1 yr-1。结果表明，使用人工神经网络方法可以较好地预测增量估计，并且人工神经网络方法能够比传统回归模型更准确地估计增量。