Neural network and GA approaches for dwelling fire occurrence prediction

Knowledge-Based Systems

Volumn 19, Issue 4, 2006, Pages 213-219

  Yang, L ^a   Dawson, C W ^a   Brown, M R ^b   Gell, M ^c  

^a LOUGHBOROUGH UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF CENTRAL LANCASHIRE   (United Kingdom)

^c UNIVERSITY OF DERBY   (United Kingdom)

Author keywords

Dwelling fire; Genetic algorithms; Neural network; Prediction; Principal component analysis

Indexed keywords

DATA PROCESSING; FIRE PROTECTION; GENETIC ALGORITHMS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; STATISTICAL METHODS;

DWELLING FIRE; PREDICTION; TEST DATA SETS;

NEURAL NETWORKS;

EID: 33745877264     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2005.11.021     Document Type: Article

References (18)

1. Fernandez-pello C. The challenge of fire prediction. Combustion Science and Technology 98 (1994) 281-290
2. Kuang-Chung T., and Drysdale D. Using cone calorimeter data for the prediction of fire hazard. Fire Safety Journal 37 7 (2002) 697-706
3. Martell D.L., Bevilacqua E., and Stocks B.J. A logistic model for predicting daily people-caused forest fire occurrence in Ontario. Canadian Journal of Forest Research 17 (1987) 394-401
4. Chou Y.H., Minnich R.A., and Chase R.A. Mapping probability of fire occurrence in San Jacinto Mountains, California, USA. Environment Management 17 (1993) 129-140
5. Vega-Garcia C., Lee B.S., Woodard P.M., Titus S.J., Adamowicz W.L., and Lee B.S. A logistic model for predicting the daily occurrence of human caused forest fires. International Journal of Wildland Fire 5 2 (1995) 101-111
6. Vega-Garcia C., Lee B.S., Woodard P.M., and Titus S.J. Applying neural network technology to human- caused wildfire occurrence prediction. AI Applications 10 3 (1996) 9-18
7. Olivas J.A. Applications of fuzzy prototypes: forest fire prediction and medical diagnosis. International Conference On Artificial Intelligence I & II (1999) 662-668
8. Diez E.L.G., Soriano L.R., de Pabloand F., and Diez A.G. Prediction of the daily number of forest fires. International Journal of Wildland Fire 9 3 (2000) 207-211
9. A. Alonso-Betazos, O. Fontenia-Romero, B. Guijarro-Berdinas, E. Hernandez-Pereira, J. Canda, E. Jimenez, J. Legido, S. Muniz, C. Paz-Andrade, M.I. Paz-Andrade, A neural network approach for forestall fire risk estimation, in: Proceedings of 15th European Conference On Artificial Intelligence, (2002) 643-647.
10. Home office, Predict fire incidence in Northampton and Northamptonshire, Internal report, Home office in the UK, 1971.
11. C. Deng, L.B. Wu, W.C. Fan, Neural network approach for RHR calculation and prediction in fire science, in: Proceedings of 3rd International Conference on Signal Processing, vols. 1 & 2, (1996) 1484-1487.
12. Faraway J., and Chatfield C. Time series forecasting with neural networks: a comparative study using the airline data. Applied Statistics 47 2 (1998) 231-250
13. Jolliffe I.T. Principal component analysis (1986), Springer, New York
14. Valle S., Li W., and Qin S. Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Industrial & Engineering Chemistry Research 38 11 (1999) 4389-4401
15. Yang L., Brown M.R., Dawson C.W., and Gell M. Prediction of dwelling fire occurrences using data mining technologies. In: Zanasi A., Brebbia C.A., Ebecken N.F.F.E., and Melli P. (Eds). Data Mining III (2002), WIT press 705-714
16. Adriaans P., and Zantinge D. Data Mining (1996), Addison-wesley, Reading, MA
17. Hall L.O., Ozyurt I.B., and Bezdek J.C. Clustering with a genetically optimised approach. IEEE Transactions on Evolutionary Computation 3 2 (1999) 103-112
18. L. Meng, Q.H. Wu, Z.Z. Yong, A faster genetic clustering algorithm, in: Lecture Notes in Computer Science, vol. 1803, (2000) 22-33.