In this study, the soft computing technique of Gene expression programming (GEP) has been employed to generate a predictive equation of infiltration rate (f_p). Infiltration experiments were conducted at 124 different sites and soil samples were collected to assess various soil properties throughout the Himalayan lake catchment. Parameters determined from observed data using nonlinear-Levenberg Marquardt algorithm were substituted in Horton, Kostiakov and Philip infiltration models and f_p were predicted. Using soil data generated by laboratory investigation of soil samples, the GEP model was developed. Training and testing of the GEP model was performed using 70% and 30% of data respectively. Performance of GEP developed functional relationship was evaluated by comparing predictions from it and aforementioned infiltration models with field observed f_p, and by applying overall performance index (OPI) computed using Coefficient of Determination (R²), Nash–Sutcliffe Efficiency (E_NS), Willmott’s Index of Agreement (W), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Expression developed using GEP indicated feasibility of developed equation with E_NS, R², W, RMSE and MAE of 0.84, 0.84, 0.96, 1.9, and 0.8, respectively for training data-set and 0.84, 0.85, 0.95, 1.2, and 0.95, respectively for testing data-set. Comparative analysis revealed that though with a slightly higher OPI value (0.7–0.8), the performance of conventional models is better compared to the GEP model (0.66) but the GEP model having satisfactory performance may be used for f_p prediction particularly in absence of observed data.

在这项研究中，已采用基因表达编程（GEP）的软计算技术来生成渗透率（f _p）的预测方程。在124个不同的地点进行了入渗实验，并收集了土壤样品以评估整个喜马拉雅湖流域的各种土壤特性。在Horton，Kostiakov和Philip渗透模型中，使用非线性Levenberg Marquardt算法根据观测数据确定的参数被替换为f _p被预测。利用实验室对土壤样品进行调查产生的土壤数据，开发了GEP模型。GEP模型的训练和测试分别使用70％和30％的数据进行。GEP所开发的功能关系的性能通过将其预测值与上述渗透模型的预测值与现场观测到的f _{p进行比较}，并应用通过测定系数（R ²），纳什-舒特克里夫效率（E _NS）计算的总体性能指数（OPI）进行评估，Willmott的一致性指数（W），平均绝对误差（MAE）和均方根误差（RMSE）。使用GEP开发的表达式表明用ESNS _， R ²开发方程的可行性，训练数据集的W，RMSE和MAE分别为0.84、0.84、0.96、1.9和0.8，测试数据集的W，RMSE和MAE分别为0.84、0.85、0.95、1.2和0.95。对比分析表明，尽管OPI值（0.7-0.8）稍高，但常规模型的性能优于GEP模型（0.66），但具有令人满意性能的GEP模型可用于f _p预测，尤其是在没有PPI的情况下。观察数据。