Predicting Size of Forest Fire Using Hybrid Model

Abstract : This paper outlines a hybrid approach in data mining to predict the size of forest fire using meteorological and forest weather index (FWI) variables such as Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), temperature, Relative Humidity (RH), wind and rain. The hybrid model is developed with clustering and classification approaches. Fuzzy C-Means (FCM) is used to cluster the historical variables. The clustered data are then used as inputs to Back-Propagation Neural Network classification. The label dataset having value greater than zero in fire area size are clustered using FCM to produce two categorical clusters,i.e.: Light Burn, and Heavy Burn for its label. On the other hand, fire area label with value zero is clustered as No Burn Area. A Back-Propagation Neural Network (BPNN) is trained based on these data to classify the output (burn area) in three categories, No Burn Area, Light Burn and Heavy Burn. The experiment shows promising results depicting classification size of forest fire with the accuracy of confusion matrix around 97, 50 % and Cohens Kappa 0.954. This research also compares the performance of proposed model with other classification method such as SVM, Naive Bayes, DCT Tree, and K-NN that showed BPNN have best performance.
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal.inria.fr/hal-01397228
Contributor : Hal Ifip <>
Submitted on : Tuesday, November 15, 2016 - 3:48:15 PM
Last modification on : Wednesday, November 16, 2016 - 1:04:11 AM
Long-term archiving on : Thursday, March 16, 2017 - 4:55:00 PM

File

978-3-642-55032-4_31_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Guruh Shidik, Khabib Mustofa. Predicting Size of Forest Fire Using Hybrid Model. 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.316-327, ⟨10.1007/978-3-642-55032-4_31⟩. ⟨hal-01397228⟩

Share

Metrics

Record views

80

Files downloads

307