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Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling

Isham Alzoubi (Department of Surveying and Geometric Engineering, Engineering Faculty, University of Tehran, Tehran, Iran)
Mahmoud Delavar (Department of Surveying and Geometric Engineering, Engineering Faculty, University of Tehran, Tehran, Iran)
Farhad Mirzaei (College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran)
Babak Nadjar Arrabi (School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran)

International Journal of Energy Sector Management

ISSN: 1750-6220

Article publication date: 6 September 2017

Issue publication date: 18 September 2017

536

Abstract

Purpose

This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling.

Design/methodology/approach

Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can 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 per cent and soil swelling index on energy consumption were investigated.

Findings

According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R2) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively.

Originality/value

A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.

Keywords

Citation

Alzoubi, I., Delavar, M., Mirzaei, F. and Nadjar Arrabi, B. (2017), "Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling", International Journal of Energy Sector Management, Vol. 11 No. 4, pp. 522-540. https://doi.org/10.1108/IJESM-01-2017-0003

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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