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M. Diamantopoulou (2005)
Artificial neural networks as an alternative tool in pine bark volume estimationComputers and Electronics in Agriculture, 48
A. Azadeh, S. Ghaderi, S. Sohrabkhani (2008)
Annual electricity consumption forecasting by neural network in high energy consuming industrial sectorsEnergy Conversion and Management, 49
M. Nikoo, F. Ramezani, M. Hadzima-Nyarko, E. Nyarko, M. Nikoo (2016)
Flood-routing modeling with neural network optimized by social-based algorithmNatural Hazards, 82
S. Shirkouhi, H. Eivazy, R. Ghodsi, K. Rezaie, E. Atashpaz-Gargari (2010)
Solving the integrated product mix-outsourcing problem using the Imperialist Competitive AlgorithmExpert Syst. Appl., 37
H. Taghavifar, A. Mardani, L. Taghavifar (2013)
A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facilityMeasurement, 46
Xiuping Zhang (2012)
The Application of Imperialist Competitive Algorithm based on Chaos Theory in Perceptron Neural NetworkPhysics Procedia, 25
R. Rallo, J. Ferré-Giné, A. Arenas, F. Giralt (2002)
Neural virtual sensor for the inferential prediction of product quality from process variablesComputers & Chemical Engineering, 26
A. Shakibai, S. Koochekzadeh (2009)
Modeling and predicting agricultural energy consumption in Iran.American-Eurasian Journal of Agricultural and Environmental Science, 5
Marjan Abdechiri, K. Faez, Helena Bahrami (2010)
Adaptive Imperialist Competitive Algorithm (AICA)9th IEEE International Conference on Cognitive Informatics (ICCI'10)
Farmanullah Khan (2007)
EFFECT OF LAND LEVELING ON SOME PHYSICO-CHEMICAL PROPERTIES OF SOIL IN DISTRICT DIR LOWER
M. Ahmadi, Mohammad Golshadi (2012)
Neural network based swarm concept for prediction asphaltene precipitation due to natural depletionJournal of Petroleum Science and Engineering
B. Mcfarlane, R. Stumpf-Allen, David Watson (2006)
Public perceptions of natural disturbance in Canada's national parks: the case of the mountain pine beetle (Dendroctonus ponderosae Hopkins).Biological Conservation, 130
M. Ahmadi, S. Shadizadeh (2012)
New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm conceptFuel, 102
J. Toro, I. Requena, M. Zamorano (2010)
Environmental impact assessment in Colombia: Critical analysis and proposals for improvementEnvironmental Impact Assessment Review, 30
Indian Journal of Fundamental and Applied Life Sciences, 5
M. Ahmadi, M. Ebadi, A. Shokrollahi, S. Majidi (2013)
Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoirAppl. Soft Comput., 13
A. Ebrahimzadeh, J. Addeh, Z. Rahmani (2012)
Control chart pattern recognition using K-MICA clustering and neural networks.ISA transactions, 51 1
Applied Soft Computing, 23
M. Ahmadi, R. Soleimani, A. Bahadori (2014)
A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systemsFuel, 137
D. Cassel, P. Wood, R. Bunge, L. Glaser (1982)
Mitogenicity of Brain Axolemma Membranes and Soluble Factors for Dorsal Root Ganglion Schwann CellsJournal of Cellular Biochemistry, 18
Hindawi Publishing Corporation the Scientific World Journal, 2014
K. Brye, N. Slaton, R. Norman (2006)
Soil Physical and Biological Properties as Affected by Land Leveling in a Clayey AquertSoil Science Society of America Journal, 70
K. Movagharnejad, Maryam Nikzad (2007)
Modeling of tomato drying using artificial neural networkComputers and Electronics in Agriculture, 59
Indian Journal of Fundamental and Applied Life Sciences, 5
B. Abdi, H. Mozafari, A. Ayob, Roya Kohandel (2011)
Imperialist competitive algorithm and its application in optimization of laminated composite structuresEuropean journal of scientific research, 55
M. Ahmadi, A. Bahadori, S. Shadizadeh (2015)
A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperatureFuel, 139
S. Talatahari, A. Kaveh, R. Sheikholeslami (2012)
Chaotic imperialist competitive algorithm for optimum design of truss structuresStructural and Multidisciplinary Optimization, 46
B. Tiryaki (2008)
Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression treesEngineering Geology, 99
R. Rajabioun, E. Atashpaz-Gargari, C. Lucas (2008)
Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement
E. Okasha, R. Abdelraouf, M. Abdou (2013)
Effect of Land Leveling and Water Applied Methods on Yield and Irrigation Water Use Efficiency of Maize (Zea mays L.) Grown under Clay Soil Conditions
A. Marto, M. Hajihassani, D. Armaghani, E. Mohamad, Ahmad Makhtar (2014)
A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural NetworkThe Scientific World Journal, 2014
M. Jat, R. Gupta, O. Erenstein, R. Ortiz (2006)
Diversifying the intensive cereal cropping systems of the Indo-Ganges through horticulture, 46
A. Kaveh, S. Talatahari (2010)
Optimum design of skeletal structures using imperialist competitive algorithmComputers & Structures, 88
S. Far, K. Rezaei-Moghaddam (2015)
Laser land levelling as a strategy for environmental management: the case of IranPolymer Journal, 1
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/approachUsing 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.FindingsAccording 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/valueA 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.
International Journal of Energy Sector Management – Emerald Publishing
Published: Sep 18, 2017
Keywords: Energy sector; Optimization; Particle swarm optimization (PSO); Neural networks; Fossil fuel; ANFIS; Land levelling; Energy; Environmental research; ANN; Sensitivity analysis; Fuzzy adaptive PSO
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