Prediction of environmental indicators in land leveling using artificial intelligence techniques

BACKGROUND: 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. The aim of this work was to determine best linear model using artificial neural network (ANN), imperialist competitive algorithm and ANN and regression and adaptive neural fuzzy inference system (ANFIS) in order to predict the environmental indicators for land leveling. METHODS: New techniques such as; ANN, imperialist competitive algorithm and ANN and sensitivity analysis and regression and ANFIS that 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 90 samples were collected from 3 different regions. The grid size was set 20 m in 20 m (20 × 20) from a farmland in Karaj province of Iran. RESULTS: According to the results of sensitivity analysis, only 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 R(2) value and lower RMSE value. Statistical factors of RMSE and R(2) illustrate the superiority of ICA-ANN over other methods by values about 0.02 and 0.99, respectively. CONCLUSION: Results extracted and statistical analysis was performed and RMSE as well as coefficient of determination, R(2), 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-Marquet as NTF. Integrating ANN and imperialist competitive algorithm (ICA-ANN) had better performance in prediction of output parameters in comparison with conventional methods such.