Abstract In the present research, gene expression programming (GEP) and artificial neural network (ANN) techniques were used to estimate soil enzyme activity (SEA), including urease, alkaline phosphatase and dehydrogenase. Data from 65 soil samples located in Mirabad region, Suldoz plain (West Azerbaijan, Iran) were used to test the adopted methodology. The soil samples were selected from areas with different land usages (apple orchard, crop production, and rich pasture). Various combinations of the input parameters including soil texture, pH, organic carbon (OC), electrical conductivity (EC), microbial biomass carbon (MBC), and microbial soil respiration (SIR) were utilized to feed the applied models. The root mean square error (RMSE) and the coefficient of determination (R2) were employed for assessing the models' performance accuracy. The highest R2 and lowest RMSE were obtained for the models that used all available input parameters. The results showed that among targets (urease activity (UA), alkaline phosphatase (ALP) and/or dehydrogenase activity (DHA)), the highest performance accuracy was obtained for urease activity models. The obtained results revealed that the most effective parameters in estimating urease activity were soil texture, pH, EC and OC; where about 69% and 68% of its variability was predictable by the ANN and GEP, respectively.

中文翻译：

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使用易于测量的变量模拟土壤酶活性：启发式替代方案

摘要 在本研究中，基因表达编程（GEP）和人工神经网络（ANN）技术被用来估计土壤酶活性（SEA），包括尿素酶、碱性磷酸酶和脱氢酶。来自位于苏尔多兹平原（西阿塞拜疆，伊朗）米拉巴德地区的 65 个土壤样本的数据用于测试所采用的方法。土壤样品选自具有不同土地用途的区域（苹果园、作物生产和肥沃的牧场）。输入参数的各种组合，包括土壤质地、pH、有机碳 (OC)、电导率 (EC)、微生物生物量碳 (MBC) 和微生物土壤呼吸 (SIR)，用于提供应用模型。均方根误差 (RMSE) 和决定系数 (R2) 用于评估模型的性能准确性。对于使用所有可用输入参数的模型，获得了最高的 R2 和最低的 RMSE。结果表明，在目标（脲酶活性 (UA)、碱性磷酸酶 (ALP) 和/或脱氢酶活性 (DHA)）中，脲酶活性模型获得了最高的性能准确度。所得结果表明，估计脲酶活性最有效的参数是土壤质地、pH、EC 和 OC；其中大约 69% 和 68% 的可变性分别由 ANN 和 GEP 预测。所得结果表明，估计脲酶活性最有效的参数是土壤质地、pH、EC 和 OC；其中大约 69% 和 68% 的可变性分别由 ANN 和 GEP 预测。所得结果表明，估计脲酶活性最有效的参数是土壤质地、pH、EC 和 OC；其中大约 69% 和 68% 的可变性分别由 ANN 和 GEP 预测。