A neural network based model for urban noise prediction.

Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then, most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context, urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise, using a wide range of approaches, but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques, namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy, and thus, was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results, the values obtained were also quite acceptable.

[1]  Dick Botteldooren,et al.  A fuzzy rule based framework for noise annoyance modeling. , 2003, The Journal of the Acoustical Society of America.

[2]  Dick Botteldooren,et al.  The effect of land-use variables in a fuzzy rule based model for noise annoyance , 2002 .

[3]  A. F. Ramos-Ridao,et al.  Modeling Environmental Noise Using Artificial Neural Networks , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[4]  A. Verkeyn,et al.  Fuzzy modeling of traffic noise annoyance , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[5]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[6]  Arslan Saral,et al.  Neural Network Modelling of Outdoor Noise Levels in a Pilot Area , 2004 .

[7]  Paul J. Werbos,et al.  The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting , 1994 .

[8]  D. Botteldooren,et al.  Fuzzy models for accumulation of reported community noise annoyance from combined sources. , 2002, The Journal of the Acoustical Society of America.

[9]  Chris Cornelis,et al.  On the Meaning of Noise Annoyance Modifiers: A Fuzzy Set Theoretical Approach , 2002 .

[10]  Hussein Dia,et al.  An object-oriented neural network approach to short-term traffic forecasting , 2001, Eur. J. Oper. Res..

[11]  A. Verkeyn,et al.  Noise Annoyance Modelling using Fuzzy Rule Based Systems. , 2002, Noise & health.

[12]  Kunihiko Fukushima,et al.  Cognitron: A self-organizing multilayered neural network , 1975, Biological Cybernetics.

[13]  Dick Botteldooren,et al.  An iterative fuzzy model for cognitive processes involved in environment quality judgement , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[14]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[15]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[16]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[17]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[18]  Dick Botteldooren,et al.  Generating Membership Functions for a Noise Annoyance Model from Experimental Data , 2003 .

[19]  Riccardo Caponetto,et al.  GAs for fuzzy modeling of noise pollution , 1997, Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97.

[20]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[21]  Gail A. Carpenter,et al.  Distributed Activation, Search, and Learning by ART and ARTMAP Neural Networks , 1996 .

[22]  Dick Botteldooren,et al.  Aggregation of specific noise annoyance to a general noise annoyance rating: a fuzzy model , 2002 .

[23]  Zaheeruddin,et al.  A neuro-fuzzy approach for prediction of human work efficiency in noisy environment , 2006, Appl. Soft Comput..

[24]  Shing Chung Josh Wong,et al.  Urban traffic flow prediction using a fuzzy-neural approach , 2002 .

[25]  Corinne Ledoux,et al.  An urban traffic flow model integrating neural networks , 1997 .

[26]  Dick Botteldooren,et al.  Annoyance prediction with fuzzy rule bases , 2002 .

[27]  Ignacio Requena,et al.  Priorization of acoustic variables: Environmental decision support for the physical characterization of urban sound environments , 2010 .

[28]  Ignacio Requena,et al.  Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE , 2006 .

[29]  Carl Tim Kelley,et al.  Iterative methods for optimization , 1999, Frontiers in applied mathematics.

[30]  Vincent Fontaine,et al.  AUTOMATIC CLASSIFICATION OF ENVIRONMENTAL NOISE EVENTS BY HIDDEN MARKOV MODELS , 1998 .

[31]  Salvatore Cavalieri,et al.  A neural network architecture for noise prediction , 1995, Neural Networks.

[32]  Systems Man Ieee.  Joint 9th Ifsa World Congress and 20th NAFIPS International Conference 2001 , 2001 .

[33]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[34]  Luigi Fortuna,et al.  A neuro-fuzzy model of urban traffic , 1994, Proceedings of 1994 37th Midwest Symposium on Circuits and Systems.

[35]  Vincent Fontaine,et al.  Automatic classification of environmental noise events by hidden Markov models , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[36]  L. Couvreur,et al.  Automatic Noise Recognition in Urban Environments Based on Artificial Neural Networks and Hidden Markov Models , .

[37]  Marco Tomassini,et al.  Soft computing - integrating evolutionary, neural, and fuzzy systems , 2001 .

[38]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[39]  Ben P. Milner,et al.  Context awareness using environmental noise classification , 2003, INTERSPEECH.

[40]  Koichi Shinoda,et al.  MODEL OPTIMIZATION FOR NOISE DISCRIMINATION IN HOME ENVIRONMENT , 2005 .

[41]  Ben P. Milner,et al.  Environmental Noise Classification for Context-Aware Applications , 2003, DEXA.

[42]  Giuseppe Ruggeri,et al.  New results in fuzzy pattern classification of background noise , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[43]  D. Botteldooren,et al.  Soft-computing base analyses of the relationship between annoyance and coping with noise and odor. , 2004, The Journal of the Acoustical Society of America.

[44]  Zoubin Ghahramani,et al.  An Introduction to Hidden Markov Models and Bayesian Networks , 2001, Int. J. Pattern Recognit. Artif. Intell..