Assessment of soil erosion is crucial for any long-term soil conservation plan. Traditional in-situ measurements provide a precise amount of erosion rate; however, the procedure is costly and time-consuming when applied over an extensive area. This study aimed to investigate the use of erosion pins and artificial neural networks (ANNs) to assess the spatial distribution of annual soil erosion rates in the mountainous areas of the north of Iran. First, annual surface erosion and splash erosion were measured using two types of erosion pins. Next, the variables affecting soil erosion (vegetation canopy, the shape of slope, slope gradient, slope length, and soil properties) were identified and estimated through field studies and analysis of a digital elevation model (DEM) and the data set were divided into three subsets of training, cross-validation, and testing. Seven artificial neural network algorithms were used and evaluated to estimate the annual soil erosion rates for the areas without recorded erosion data. Finally, the modeled values were mapped in GIS, and the longitudinal profiles of soil erosion were extracted. Findings showed that (1) Consideration should be given to the generalized feed forward (GFF) network, given the high accuracy rate (NMSE:0.1; R-sqr:0.9) compared to other tested ANN algorithms. (2) Vegetation canopy was found to be the most significant variable in annual soil erosion rate (R: −0.75 to −0.85) compared to other input variables. And (3) Annual measurements of erosion pins revealed that the splash erosion is higher (contributing 62 percent to total erosion) compared to surface runoff erosion (contributing 38 percent to total erosion).

对土壤侵蚀的评估对于任何长期的土壤保持计划都是至关重要的。传统的原位测量提供了精确的腐蚀速率。然而，当在大面积上应用时，该过程既昂贵又费时。这项研究旨在调查使用侵蚀针和人工神经网络（ANN）评估伊朗北部山区年土壤侵蚀率的空间分布。首先，使用两种类型的侵蚀针来测量年度表面侵蚀和飞溅侵蚀。接下来，通过田间研究和数字高程模型（DEM）的分析，确定并估算了影响土壤侵蚀的变量（植被冠层，坡度，坡度，坡度和土壤特性），并将数据集分为训练的三个子集，交叉验证，和测试。使用了七个人工神经网络算法并进行了评估，以估算没有记录侵蚀数据的地区的年土壤侵蚀率。最后，将模型值映射到GIS中，并提取土壤侵蚀的纵向剖面。研究结果表明：（1）考虑到与其他经过测试的ANN算法相比具有较高的准确率（NMSE：0.1; R-sqr：0.9），应该考虑广义前馈（GFF）网络。（2）与其他输入变量相比，植被冠层是年土壤侵蚀率中最显着的变量（R：-0.75至-0.85）。（3）侵蚀销钉的年度测量结果表明，与地表径流侵蚀（占总侵蚀的38％）相比，飞溅侵蚀更高（占总侵蚀的62％）。使用了七个人工神经网络算法并进行了评估，以估算没有记录侵蚀数据的地区的年土壤侵蚀率。最后，将模型值映射到GIS中，并提取土壤侵蚀的纵向剖面。研究结果表明：（1）考虑到与其他经过测试的ANN算法相比具有较高的准确率（NMSE：0.1; R-sqr：0.9），应该考虑广义前馈（GFF）网络。（2）与其他输入变量相比，植被冠层是年土壤侵蚀率中最显着的变量（R：-0.75至-0.85）。（3）侵蚀销钉的年度测量结果表明，与地表径流侵蚀（占总侵蚀的38％）相比，飞溅侵蚀更高（占总侵蚀的62％）。使用了七个人工神经网络算法并进行了评估，以估算没有记录侵蚀数据的地区的年土壤侵蚀率。最后，将模型值映射到GIS中，并提取土壤侵蚀的纵向剖面。研究结果表明：（1）考虑到与其他经过测试的ANN算法相比具有较高的准确率（NMSE：0.1; R-sqr：0.9），应该考虑广义前馈（GFF）网络。（2）与其他输入变量相比，植被冠层是年土壤侵蚀率中最显着的变量（R：-0.75至-0.85）。（3）侵蚀销钉的年度测量结果表明，与地表径流侵蚀（占总侵蚀的38％）相比，飞溅侵蚀更高（占总侵蚀的62％）。