TY - GEN
T1 - Effect of weather conditions, geography and population density on wildfire occurrence
T2 - 11th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP
AU - Papakosta, P.
AU - Straub, D.
PY - 2011
Y1 - 2011
N2 - Occurrences of wildfires are related to weather conditions and human intervention and can only be predicted probabilistically. In this paper, the potential of Bayesian Networks for such predictions is investigated. A Bayesian Network is constructed, which expresses the effect of weather conditions, land cover and human presence on the rate of wildfire occurrences. The model is based on both temporal and spatial data. The parameters of the model are inferred from data obtained for the Greek Mediterranean island of Rhodes. Initial results show a dependence between human population density and wildfire occurrence. The selected indicator for weather conditions, a commonly used fuel moisture index, is found to be ill-suited for predicting wildfire occurrence on Rhodes, possibly due to the specifics of the Mediterranean climate. Future work is needed to identify and include relevant influencing factors, which is facilitated by the Bayesian network modeling approach.
AB - Occurrences of wildfires are related to weather conditions and human intervention and can only be predicted probabilistically. In this paper, the potential of Bayesian Networks for such predictions is investigated. A Bayesian Network is constructed, which expresses the effect of weather conditions, land cover and human presence on the rate of wildfire occurrences. The model is based on both temporal and spatial data. The parameters of the model are inferred from data obtained for the Greek Mediterranean island of Rhodes. Initial results show a dependence between human population density and wildfire occurrence. The selected indicator for weather conditions, a commonly used fuel moisture index, is found to be ill-suited for predicting wildfire occurrence on Rhodes, possibly due to the specifics of the Mediterranean climate. Future work is needed to identify and include relevant influencing factors, which is facilitated by the Bayesian network modeling approach.
UR - http://www.scopus.com/inward/record.url?scp=84856730453&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84856730453
SN - 9780415669863
T3 - Applications of Statistics and Probability in Civil Engineering -Proceedings of the 11th International Conference on Applications of Statistics and Probability in Civil Engineering
SP - 335
EP - 342
BT - Applications of Statistics and Probability in Civil Engineering -Proceedings of the 11th International Conference on Applications of Statistics and Probability in Civil Engineering
Y2 - 1 August 2011 through 4 August 2011
ER -