.png){kind=icon}
Return
Flash flood susceptibility mapping using stacking ensemble machine learning models
.png){kind=icon}
PRE
AI
DOI：10.1080/10106049.2022.2093990.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The objective of the present study was to introduce a novel methodological approach for flash flood susceptibility modeling based on a stacking ensemble (SE) model. Two SE models, Random Forest (RF) and Artificial Neural Network (ANN) were developed, whereas LDA, CART, LR, k-NN and SVM were the basic models of the two SE models. The performance of the developed methodology was evaluated at the Island of Rhodes, Greece. The database included 54 flash floods locations and 14 flood-related parameters. The SE-RF model produced slightly higher predictive results, in terms of accuracy (0.844), kappa index (0.687) and the area under the receiver operating characteristic curve (0.870), followed by the SE-ANN with values of 0.812, 0.625 and 0.773, respectively. Overall, the study provides evidence about the higher accuracy SE models can achieve since they are capable of combining in an intelligent way a number of weak predictive models.
Keywords:
flood susceptibility
stacking ensemble models
random forest
neural network
island of Rhodes
Greece
Journal
IF:
3.5
Papers:
2.4K
Citations:
6.9K
Organization
Cited Papers
ShareSaveShareSavePrediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran.png)
SUSTAINABILITY
OAAI
SUSTAINABILITYIF3.3
OAAIShareSaveShareSaveSpatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GISJOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
ShareSave
View more