Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility
- This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independentThis study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.…
Download full text files
Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.
Additional Services
| Document Type: | Article |
|---|---|
| Author: | Shahab S. BandORCiD, Saeid JanizadehORCiD, Subodh Chandra PalORCiD, Asish SahaORCiD, Rabbin ChakraborttyORCiD, Manouchehr ShokriORCiD, Amir Hosein MosaviORCiD |
| DOI (Cite-Link): | https://doi.org/10.3390/s20195609Cite-Link |
| URN (Cite-Link): | https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210122-43341Cite-Link |
| URL: | https://www.mdpi.com/1424-8220/20/19/5609 |
| Parent Title (English): | Sensors |
| Publisher: | MDPI |
| Place of publication: | Basel |
| Language: | English |
| Date of Publication (online): | 2021/01/19 |
| Date of first Publication: | 2020/09/30 |
| Release Date: | 2021/01/22 |
| Publishing Institution: | Bauhaus-Universität Weimar |
| Institutes and partner institutions: | Fakultät Bauingenieurwesen / Institut für Strukturmechanik (ISM) |
| Volume: | 2020 |
| Issue: | Volume 20, issue 19, article 5609 |
| Pagenumber: | 27 |
| First Page: | 1 |
| Last Page: | 27 |
| Tag: | OA-Publikationsfonds2020 deep learning neural network; gully erosion susceptibility; partical swarm optimization |
| GND Keyword: | Geoinformatik; Maschinelles Lernen |
| Dewey Decimal Classification: | 500 Naturwissenschaften und Mathematik / 510 Mathematik |
| BKL-Classification: | 31 Mathematik |
| Open Access Publikationsfonds: | Open-Access-Publikationsfonds 2020 |
| Licence (German): |  Creative Commons 4.0 - Namensnennung (CC BY 4.0) |