Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines require considerable amount of energy, it delivers a suitable surface slope with minimal deterioration of the soil and damage to plants and other organisms in the soil. Notwithstanding, researchers during recent years have tried to reduce fossil fuel consumption and its deleterious side effects during this operation. The aim of this work was to determine the best linear model using Artificial Neural Network (ANN), Imperialist Competitive Algorithm–ANN, regression, and Adaptive Neural Fuzzy Inference System (ANFIS) to predict the environmental indicators for land leveling and to determine a model to estimate the dependence degree of parameters on each other. New techniques such as ANN, ICA, GWO–ANN, PSO–ANN, sensitivity analysis, regression, and ANFIS that using them for optimizing energy consumption will lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand percent, and soil swelling index in energy consumption were investigated. The study was consisted of 350 samples which were collected from 175 regions in two depths. The grid size was set 20 m × 20 m from a 70-ha farmland in Karaj province of Iran. The models that reveals the relationship between the land parameters and the energy indicators were extracted. As it was expected three parameters; density, soil compressibility factor and, embankment volume index had significant effect on fuel consumption. In comparison with ANN, all ICA–ANN models had higher accuracy in prediction according to their higher R2 value and lower RMSE value. Statistical factors of RMSE and R2 illustrate the superiority of ICA–ANN over other methods by values about 0.02 and 0.99, respectively. Results also revealed the superiority of integrated techniques over other methods for prediction of complicated problems such as land leveling energy estimation. Results were extracted and statistical analysis was performed, and RMSE as well as coefficient of determination, R2, of the models were determined as a criterion to compare selected models. According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1, and 10-6-4-1 MLP network structures were chosen as the best arrangements and were trained using Levenberg–Marquardt as NTF. Integrating ANN and imperialist competitive algorithm (ICA–ANN) had the best performance in prediction of output parameters, i.e., energy indicators.

中文翻译：

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使用人工智能技术预测土地平整中的环境指标

土地平整是整地和耕作中最重要的步骤之一。尽管用机器进行土地平整需要大量能量，但它提供了合适的表面坡度，使土壤变质最小，并且对土壤中的植物和其他生物造成了破坏。尽管如此，近年来，研究人员已尝试减少该操作期间的化石燃料消耗及其有害副作用。这项工作的目的是使用人工神经网络（ANN），帝国主义竞争算法–ANN，回归和自适应神经模糊推理系统（ANFIS）来确定最佳线性模型，以预测土地平整的环境指标并确定模型估计参数之间的依赖程度。新技术，例如ANN，ICA，GWO–ANN，PSO–ANN，敏感性分析，回归和ANFIS（使用它们来优化能耗）将导致环境的显着改善。在这项研究中，研究了路堤体积，土壤可压缩性因子，比重，水分，坡度，含沙量和土壤溶胀指数等各种土壤特性对能耗的影响。该研究由350个样品组成，分别从175个区域的两个深度采集。网格大小设置为来自伊朗卡拉伊省一个70公顷农田的20 m×20 m。提取了揭示土地参数与能源指标之间关系的模型。如预期的那样，三个参数；密度，土壤压缩系数和路堤体积指数对燃料消耗有显着影响。与人工神经网络相比，所有ICA-ANN模型的R2值较高且RMSE值较低，因此预测精度较高。RMSE和R2的统计因子分别以0.02和0.99的值说明了ICA–ANN在其他方法上的优越性。结果还显示了集成技术优于其他方法来预测诸如土地平整能量估算之类的复杂问题。提取结果并进行统计分析，并确定模型的RMSE以及确定系数R2作为比较所选模型的标准。根据结果​​，选择10-8-3-1、10-8-2-5-1、10-5-8-10-1和10-6-4-1 MLP网络结构是最佳安排并接受了Levenberg-Marquardt作为NTF的培训。