TY  - JOUR
T1  - Machine Learning Regression Techniques to Predict Burned Area of Forest Fires
AU - M. Elshewey, Ahmed 
JO  - International Journal of Soft Computing
VL  - 16
IS  - 1
SP  - 1
EP  - 8
PY  - 2021
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2021.1.8
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2021.1.8
KW  - Lasso regression
KW  -ridge regression
KW  -linear regression
KW  -Machine learning
KW  -forest fires
KW  -algorithms
AB  - The study presents the implementation of
machine learning regression techniques to predict burned
areas of forest fires. The data set used in this paper is
presented in UCI machine learning repository that
consists of climatic conditions and physical factors of the
Montesinhopark in Portugal. Linear regression, ridge
regression and lasso regression algorithms are used in the
process of prediction. Accuracy score, Mean Absolute
Error (MAE), Median Absolute Error (MDAE) and Mean
Squared Error (MSE) were calculated. The size of the data
set is 517 entries and the number of features for each row
is 13. In this study the three algorithms are applied using
two versions. In the first version, all features of the data
set were included and in the second version, 70% of the
features were included. In both versions, the training set
is 70% of the data set and the test set is 30% of the data
set. The accuracy of linear regression algorithm is 100%,
thus it gives more accuracy than ridge regression and
lasso regression algorithms in both versions.
ER  -