This study aims to determine the effect of a flood-spreading system on the morphometric characteristics of alluvial fans (AFs) in Gribayegan Fasa, Iran, and its relationship with erosion, age, texture, and type of formations. After determining the AFs using the semiautomatic method and determining their recharged watersheds, 25 morphometric characteristics were investigated. The most important morphometric characteristics were identified using principal component analysis (PCA). The group method of data handling (GMDH) neural network is used to predict erosion, soil texture, age, and formation material based on the morphometric characteristics of the fan. The results demonstrate that the semiautomatic method can effectively extract AF from the landscape. The results of AF morphometry before and after flood spreading show that the fan area, drainage basin circularity (C_irb), fan perimeter, relief ratio of the fan, fan length, minimum fan height, and maximum fan height were higher before flood spreading, and basin shape, S_t (soil texture), upper fan slope, fan volume, and sweep angle had higher values after the flood. In addition, the results of PCA show that fan area, fan perimeter, fan length, fan radius, fan volume, basin area, basin perimeter, main channel length, basin length, and drainage density are important factors. The results of the GMDH algorithm reveal that this method can accurately predict the formation, age, soil texture, and erosion rate using morphometric characteristics. Therefore, the R² is 0.92 for the formation age and R² is 0.86 for the erosion rates, formation types, and soil texture, respectively. Therefore, the most important morphometric parameters can be determined using PCA, and the conditions and processes in the basin, such as formation material, age, soil texture, and erosion rate, can be predicted with high accuracy using the GMDH algorithm.

这项研究的目的是确定洪水扩散系统对伊朗Gribayegan Fasa冲积扇形态特征的影响及其与侵蚀，年龄，质地和地层类型的关系。在使用半自动方法确定AF并确定其补给分水岭之后，研究了25种形态特征。使用主成分分析（PCA）确定了最重要的形态特征。数据处理（GMDH）神经网络的分组方法用于根据风扇的形态特征来预测侵蚀，土壤质地，年龄和地层材料。结果表明，半自动方法可以有效地从景观中提取AF。洪水扩散前后的AF形态测量结果表明，风扇区域，C _irb），风扇周长，风扇的泄压比，风扇长度，最小风扇高度和最大风扇高度在洪水蔓延之前以及盆形，S _t（土壤质地），上部风扇斜率，风扇体积和扫掠度较高洪水后，角有较高的值。另外，PCA的结果表明，风扇面积，风扇周长，风扇长度，风扇半径，风扇体积，流域面积，流域周长，主通道长度，流域长度和排水密度是重要因素。GMDH算法的结果表明，该方法可以使用形态计量学特征准确预测地层，年龄，土壤质地和侵蚀速率。因此，对于形成年龄，R ²为0.92 ，R ²为0.92。侵蚀率，地层类型和土壤质地分别为0.86。因此，可以使用PCA确定最重要的形态参数，并且可以使用GMDH算法高精度地预测盆地中的条件和过程，例如地层材料，年龄，土壤质地和侵蚀速率。