Volume 8, Issue 2 (2020)
ECOPERSIA 2020, 8(2): 65-76 |
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Adineh F, Motamedvaziri B, Ahmadi H, Moeini A. Successive Intermodal Ensembling: A Promising Approach to Improve the Performance of Data Mining Models for Landslide Susceptibility Assessment (A case study: Kolijan Rostaq Watershed, Iran). ECOPERSIA 2020; 8 (2) :65-76
URL: http://ecopersia.modares.ac.ir/article-24-36478-en.html
URL: http://ecopersia.modares.ac.ir/article-24-36478-en.html
1- Forest, Range & Watershed Management Department, Natural Resources and Environment Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Forest, Range & Watershed Management Department, Natural Resources and Environment Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran ,bmvaziri@gmail.com
3- Reclamation of Arid & Mountainous Regions Department, Agriculture Faculty, University of Tehran, Karaj, Iran
2- Forest, Range & Watershed Management Department, Natural Resources and Environment Faculty, Science and Research Branch, Islamic Azad University, Tehran, Iran ,
3- Reclamation of Arid & Mountainous Regions Department, Agriculture Faculty, University of Tehran, Karaj, Iran
Abstract: (3149 Views)
Aims: In the present study, random forest (RF) and support vector machine (SVM) were used to assess the applicability of ensemble modeling in landslide susceptibility assessment across the Kolijan Rostaq Watershed in Mazandaran Province, Iran.
Materials & Methods: Both models were used in two modeling modes: 1) A solitary use (i.e., SVM and RF) and 2) Their ensemble with a bivariate statistical model named the weights of evidence (WofE) which then generated two more models, namely SVM-WofE and RF-WofE. Further, the resulting maps of each stage were dually coupled using the weighted arithmetic mean operation and an intermodal blending of the previous stages.
Findings: Accuracy of the models was assessed via the receiver operating characteristic (ROC) curves based on which the goodness-of-fit of the SVM and the SVM-WofE models were 0.817 and 0.841, respectively, while their respective prediction accuracy values were found to be 0.848 and 0.825. The goodness-of-fit of the RF and the RF-WofE models respectively was 0.9 and 0.823, while their respective prediction accuracy values were found to be 0.886 and 0.823. The goodness-of-fit and prediction power of SVM and SVM-WofE ensemble were respectively 0.859 and 0.873. The same increasing pattern was evident for the ensemble of RF and RF-WofE where their goodness-of-fit and prediction power increased, respectively, up to 0.928 and 0.873. Moreover, the goodness-of-fit and prediction power of RF-SVM ensemble were increased up to 0.932 and 0.899, respectively. The results of the averaged Kappa values throughout a 10-fold cross-validation test as an auxiliary accuracy assessment attested to the same results obtained from the ROC curves.
Conclusion: Successive intermodal ensembling approach is a simple and self-explanatory method so far as the context of many data mining techniques with a highly complex structure has been simply benefitted from the weighted averaging technique.
Materials & Methods: Both models were used in two modeling modes: 1) A solitary use (i.e., SVM and RF) and 2) Their ensemble with a bivariate statistical model named the weights of evidence (WofE) which then generated two more models, namely SVM-WofE and RF-WofE. Further, the resulting maps of each stage were dually coupled using the weighted arithmetic mean operation and an intermodal blending of the previous stages.
Findings: Accuracy of the models was assessed via the receiver operating characteristic (ROC) curves based on which the goodness-of-fit of the SVM and the SVM-WofE models were 0.817 and 0.841, respectively, while their respective prediction accuracy values were found to be 0.848 and 0.825. The goodness-of-fit of the RF and the RF-WofE models respectively was 0.9 and 0.823, while their respective prediction accuracy values were found to be 0.886 and 0.823. The goodness-of-fit and prediction power of SVM and SVM-WofE ensemble were respectively 0.859 and 0.873. The same increasing pattern was evident for the ensemble of RF and RF-WofE where their goodness-of-fit and prediction power increased, respectively, up to 0.928 and 0.873. Moreover, the goodness-of-fit and prediction power of RF-SVM ensemble were increased up to 0.932 and 0.899, respectively. The results of the averaged Kappa values throughout a 10-fold cross-validation test as an auxiliary accuracy assessment attested to the same results obtained from the ROC curves.
Conclusion: Successive intermodal ensembling approach is a simple and self-explanatory method so far as the context of many data mining techniques with a highly complex structure has been simply benefitted from the weighted averaging technique.
Article Type: Original Research |
Subject:
Watershed Management
Received: 2019/09/19 | Accepted: 2019/12/7 | Published: 2020/05/19
Received: 2019/09/19 | Accepted: 2019/12/7 | Published: 2020/05/19
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