Land subsidence is a complicated hazard that artificial intelligence models can model it without approximation and simplification. In this study, for the first time in land subsidence studies, we used and compared the accuracy and efficiency of hybrid fuzzy-gene expression programming (F-GEP) and fuzzy-artificial neural network (F-ANN) models in estimating land subsidence susceptibility modeling in Varamin aquifer of Iran. For this purpose, after selecting and gathering information from fifteen geo-environmental and hydrogeological effectual factors including specific yield, erosion, aquifer thickness, distance of fault, bedrock level, digital elevation model (DEM), annual rainfall, clay thickness, transmissivity (T), soil type, Debi zonation of pumping wells, slope based on DEM, groundwater drawdown in 20 years, land use, and lithological units event based on literature review in the GIS environment, they were first standardized with GIS fuzzy membership functions, and then GEP model was used to integrate the layers. For this step, using 70% of the data (2919 pixels) for the train and 30% (1251 pixels) for the test. Finally, using several statistical criteria and radar image data, the models were validated. We repeat the model on ANN, and our results showed that F-GEP model (with R² = 0.99 and RMSE = 0.004) is more accurate than F-ANN model (with R² = 0.94 and RMSE = 0.056) for land subsidence susceptibility modeling in the study area.

地面沉降是一个复杂的危害，人工智能模型可以对其进行建模，而无需进行近似和简化。在这项研究中，我们首次在土地沉降研究中使用并比较了混合模糊基因表达编程（F-GEP）和模糊人工神经网络（F-ANN）模型在估计土地沉降敏感性中的准确性和效率。在伊朗瓦拉明蓄水层中进行建模。为此，在从15种地质环境和水文地质影响因素中选择并收集了信息之后，这些因素包括比产量，侵蚀，含水层厚度，断层距离，基岩水位，数字高程模型（DEM），年降雨量，黏土厚度，透射率（T ），土壤类型，抽水井的Debi分区，基于DEM的坡度，20年的地下水汲水量，土地利用，在GIS环境下基于文献回顾的岩性单元事件，首先使用GIS模糊隶属函数对其进行标准化，然后使用GEP模型对各层进行集成。对于此步骤，将70％的数据（2919像素）用于训练，将30％（1251像素）用于测试。最后，使用几种统计标准和雷达图像数据对模型进行了验证。我们在ANN上重复该模型，结果表明F-GEP模型（带有R 对于研究区域的地面沉降敏感性模型，²  = 0.99和RMSE = 0.004）比F-ANN模型（R ² = 0.94和RMSE = 0.056）更准确。