Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- Journal of Hydrology, 2020, 590
- Issue Date:
- 2020-11-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0022169420308830-main.pdf | Published version | 15.65 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2020 Elsevier B.V. Floods are among the deadliest natural hazards for humans and the environment. Identifying the most flood-susceptible areas is a fundamental step in the development of flood mitigation strategies and for reducing flood damage. There is an ongoing global debate regarding the most suitable model for flood-susceptibility modeling and predictions. There is also a growing interest in the development of parsimonious and precise models for flood-susceptibility prediction. This study proposed several novel hybrid intelligence models based on the meta-optimization of the support vector regression (SVR) and group method of data handling (GMDH) using different meta-heuristic algorithms, i.e., the genetic algorithm (GA) and harmony search (HS). In contrast to the traditional models, in the SVR model computational complexity does not depend on the dimensionality of the input space. GMDH model has also advantage of being appropriate to analyze multi-parametric data sets. The methodology was developed for the Haraz-Neka watershed, one of the most flood-prone areas in the coastal margins of the Caspian Sea. A total of nine geospatial parameters (slope degree, aspect, elevation, plan curvature, profile curvature, distance to the river, land use, lithology, and rainfall) were identified as the main flood-conditioning factors using information gain ratio (IGR) analyses. Based on existing reports, 132 flood locations were identified in the study area, 92 points (70%) were used together with geospatial data for flood-susceptibility modeling, and the remaining 40 points (30%) were used to validate the models. An initial flood-susceptibility model was constructed based on the SVR and GMDH models. The model parameters were optimized using the GA and HS to reproduce the flood-susceptibility maps. The prediction accuracy of the resultant maps was evaluated in terms of various statistical measures, i.e., mean square error (MSE), root mean square error (RMSE), receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC). The results showed that the SVR model had superior performance (AUC = 0.70–0.75, RMSE = 0.29–0.36, MSE = 0.08–0.13) compared to the GMDH model (AUC = 0.67–0.74, RMSE = 0.32–0.39, MSE = 0.1–0.15). Both the GA and HS remarkably improved the SVR and GMDH performance, with the SVR-GA model performing the best (AUC = 0.75, RMSE = 0.29, MSE = 0.08) followed by the SVR-HS model (AUC = 0.75, RMSE = 0.33, MSE = 0.11). Our results verify the efficiency of the proposed hybrid models for spatial flood-susceptibility prediction. The proposed models can be adopted for use in other regions with similar hydro-environment characteristics.
Please use this identifier to cite or link to this item: