Predictive models for hotspots occurrence using decision tree algorithms and logistic regression.
Predictive models for hotspots (active fires) occurrence are essential to develop as one of activities in forest fires prevention in order to minimize damages because of forest fires. This work applied the decision tree algorithms i.e., ID3 and C4.5, as well as logistic regression on spatial data of...
Main Authors: | Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin |
---|---|
Format: | Article |
Language: | English English |
Published: |
Asian Network for Scientific Information
2013
|
Online Access: | http://psasir.upm.edu.my/id/eprint/29146/1/Predictive%20models%20for%20hotspots%20occurrence%20using%20decision%20tree%20algorithms%20and%20logistic%20regression.pdf |
Similar Items
-
Classification model for hotspot occurrences using spatial decision tree algorithm
by: Sitanggang, Imas Sukaesih, et al.
Published: (2013) -
A decision tree based on spatial relationships for predicting hotspots in peatlands
by: Sitanggang, Imas Sukaesih, et al.
Published: (2014) -
An extended ID3 decision tree algorithm for spatial data
by: Sitanggang, Imas Sukaesih, et al.
Published: (2011) -
Modeling forest fires risk using spatial decision tree
by: Yaakob, Razali, et al.
Published: (2011) -
Extended spatial decision tree algorithm for classifying hotspot occurrence
by: Sitanggang, Imas Sukaesih
Published: (2013)