Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration |
| |
| Authors: | Roohollah Noori Gholamali Hoshyaripour Khosro Ashrafi Babak Nadjar Araabi |
| |
| Institution: | 1. Department of Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Tehran, Iran;2. Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;1. Departamento de Producción Agraria ETSI Agrónomos, Universidad Politécnica de Madrid, UPM, Avd. Puerta de Hierro, 2, 28040 Madrid, Spain;2. Facultad de Ciencias, Universidad Complutense de Madrid, Ciudad Universitaria, Plaza Ciencias, 1, 28040 Madrid, Spain;3. Nevada Field Office, The Nature Conservancy, One E. First Street, Suite 1007, Reno, NV 89501, USA;1. Civil and Structural Engineering Department, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia;2. Dams and Water Resources Department, College Of Engineering, University of Anbar, Ramadi, Iraq;3. Department of Civil Engineering, Razi University, Kermanshah, Iran;4. School of Agricultural, Computational and Environmental Sciences, Institute of Agriculture and Environment (IAg & E), University of Southern Queensland, Springfield, QLD 4300, Australia;5. Department of Civil Engineering, Near East University, 99138, Nicosia, North Cyprus, Mersin 10, Turkey;6. UFR S2ATA « Sciences Agronomiques, d’Aquaculture et des Technologies Alimentaires », Université Gaston Berger (UGB) BP 234-Saint Louis, Senegal;7. Department of Food, Agricultural and Biological Engineering, The Ohio State University, 590 Woody Hayes Dr., Columbus, OH 43210, USA;8. Civil Engineering Department, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;9. Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering, Texas A&M University, 2117 TAMU, College Station, TX 77843-2117, USA;1. Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India;2. Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha 769008, India;3. Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110016, India;1. Department of Civil Engineering, University of Mazandaran, Babolsar, Iran;2. Faculty of Built Environment, University of New South Wales, Sydney, Australia;3. University of Michigan and Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, China;4. Civil Engineering Department, Faculty of Engineering, Antalya Bilim University, Antalya, Turkey;1. Department of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University, Tehran, Iran;2. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN 47907-2051, USA;3. Department of Civil Engineering, Monash University, Melbourne, Australia |
| |
| Abstract: | This study aims to predict daily carbon monoxide (CO) concentration in the atmosphere of Tehran by means of developed artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Forward selection (FS) and Gamma test (GT) methods are used for selecting input variables and developing hybrid models with ANN and ANFIS. From 12 input candidates, 7 and 9 variables are selected using FS and GT, respectively. Evaluation of developed hybrid models and its comparison with ANN and ANFIS models fed with all input variables shows that both FS and GT techniques reduce not only the output error, but also computational cost due to less inputs. FS–ANN and FS–ANFIS models are selected as the best models considering R2, mean absolute error and also developed discrepancy ratio statistics. It is also shown that these two models are superior in predicting pollution episodes. Finally, uncertainty analysis based on Monte-Carlo simulation is carried out for FS–ANN and FS–ANFIS models which shows that FS–ANN model has less uncertainty; i.e. it is the best model which forecasts satisfactorily the trends in daily CO concentration levels. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
