Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Building neural networks
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Efficient forest fire occurrence prediction for developing countries using two weather parameters
Engineering Applications of Artificial Intelligence
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In this paper, we propose two statistics based predictive geo-spatial data mining methods and apply them to predict the forest fire hazardous area. The proposed prediction models used in geo-spatial data mining are likelihood ratio and conditional probability methods. In these approaches, the prediction models and estimation procedures depend on the basic quantitative relationships of geo-spatial data sets relevant to the forest fire with respect to the selected areas of previous forest fire ignition. In order to make the prediction map for the forest fire hazardous area prediction map using the two proposed prediction methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. When the prediction power of the two proposed prediction models is compared, the likelihood ratio method is more powerful than the conditional probability method. The proposed model for prediction of the forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrences and effective placement of forest fire monitoring equipment and manpower.