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Reseach Article

An Interval Tree Approach to Predict Forest Fires using Meteorological Data

by Dima Alberg
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 4
Year of Publication: 2015
Authors: Dima Alberg
10.5120/ijca2015907398

Dima Alberg . An Interval Tree Approach to Predict Forest Fires using Meteorological Data. International Journal of Computer Applications. 132, 4 ( December 2015), 17-22. DOI=10.5120/ijca2015907398

@article{ 10.5120/ijca2015907398,
author = { Dima Alberg },
title = { An Interval Tree Approach to Predict Forest Fires using Meteorological Data },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 4 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number4/23582-2015907398/ },
doi = { 10.5120/ijca2015907398 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:15.273610+05:30
%A Dima Alberg
%T An Interval Tree Approach to Predict Forest Fires using Meteorological Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 4
%P 17-22
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Interval prediction can be more useful than single value prediction in many continuous data streams. This paper introduces a novel Interval Prediction Tree IP3 algorithm for interval prediction of numerical target variables from temporal mean-variance aggregated continuous data. This algorithm characterized by: processing incoming mean-variance aggregated multivariate temporal data, splitting each of the continuous features of the input according to the best mean-variance and making stable interval predictions of a target numerical variable with a given degree of statistical confidence. As shown by empirical evaluations in forest fires data set the proposed method provides better performance than existing regression tree models.

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Index Terms

Computer Science
Information Sciences

Keywords

Interval Prediction Mean-Variance Aggregation Prediction Tree Forest Fires.