# Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{8}

^{9}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Study Area

#### 2.2. Data

#### 2.2.1. Landslide-Detection-Related Dataset

#### 2.2.2. Factors Influencing Landslide Susceptibility

**Elevation**. Surface topography considerably affects the density and spatial extent of landslides by controlling the flow direction and soil moisture [81]. The minimum and maximum heights of the region can be defined using the elevation. A digital elevation model (DEM) with a spatial resolution of 30 m was downloaded from the United States Geological Survey website (Figure 4a). Other factors, i.e., the slope (Figure 4b), plan curvature (Figure 4c), valley depth (Figure 4d), and TWI (Figure 4e) were derived from the DEM.

**Slope**. The slope, which defines the steepness of the ground, affects landslide occurrence [82] owing to its influence on the moisture concentration, land instability, and hydraulic continuity.

**Plan Curvature**. The curvature indicates the land geometry and slope variation [83]. Negative, positive, and zero curvatures indicate concave, convex, and flat regions, respectively. Ohlmacher [84] highlighted the strong correlation between landslide susceptibility, type of landslide, and plan curvature of the region.

**Valley Depth**. The valley depth refers to the vertical distance of a point from the base level of the channel network [85]. This factor controls the slope stability, transportation and accumulation of water, as well as the weathering process, thereby affecting landslide occurrences [86].

**TWI**. The TWI indicates the soil moisture [87]. Landslide occurrence probability is negatively related to the TWI. Specifically, landslide susceptibility increases as cohesion decreases due to moisture loss.

**Land cover**. Land cover represents human activities and land cover variations, which considerably affect landslide occurrences [88]. For instance, the presence of vegetation may increase water accumulation, thereby decreasing the slope stability [89]. In this study area, the land cover cases involve trees, grassland, cropland, built-up area, barren land, and open water (Figure 4f). The land cover map for 2018 was obtained from the Sentinel-2A satellite image operated by the European Space Agency.

**Rainfall**. Rainfall intensity considerably affects landslide occurrences. Li et al. [90] comprehensively analyzed the effect of rainfall on earthquake-induced landslide occurrence. The mean annual rainfall map (2015–2020) was generated by the Climate Hazards Group infraRed Precipitation with Stations dataset (https://chc.ucsb.edu/data/chirps) (Accessed on 11 September 2023) (Figure 4g).

**Distance to Rivers**. Rivers may generate cuts in rocks. Moreover, the distance from rivers affects the slope stability as the saturation degree of the slope-forming materials varies with this distance [86]. According to Huang et al. [91], the probability of landslide occurrence increases with the proximity to rivers. Figure 4h shows the map of the distance to rivers.

**Distance to roads**. Roads are often the sources of slope instability and continuity. The flow of water can be altered by a road segment, which can function as a barrier, source, sink, or corridor, and its location in the mountains often results in it triggering landslides. The Open Street Map portal (https://www.openstreetmap.org/) (Accessed on 11 September 2023) was used to identify the road and river locations (Figure 4i). The distances to rivers and roads were measured in the ArcGIS Environment using the “Euclidean distance” spatial analyst tool.

#### 2.3. Landslide-Detection

#### 2.3.1. U-Net

#### 2.3.2. VGG Models

#### 2.4. Landslide Susceptibility Mapping

#### 2.4.1. FR

#### 2.4.2. CNN

#### 2.4.3. ICA

#### 2.4.4. GWO

#### 2.5. Accuracy Assessment

#### 2.5.1. Performance Assessment of Landslide Detection Algorithms

#### 2.5.2. Assessment of Landslide Susceptibility Maps

## 3. Results

#### 3.1. Correlation among the Landslide Location and Related Factors

#### 3.2. Landslide Detection

#### 3.3. Landslide Susceptibility Mapping

#### 3.4. Model Performance

## 4. Discussion

## 5. Conclusions

- Landslide inventory locations were extracted from Sentinel satellite imagery related to the Iburi earthquake (2018) in northern Japan using U-Net, VGG-16, and VGG-19.
- Three DL methods, i.e., CNN, CNN-ICA, and CNN-GWO, were used to map the landslide-prone areas.
- The outcomes were evaluated using the precision, recall, and F1-score. VGG-16 yielded the most accurate inventory map. Therefore, it was used in landslide susceptibility mapping.
- The precision and reliability of the landslide susceptibility maps were evaluated using AUROC and RMSE. CNN-GWO, with the lowest MSE (0.080), RMSE (0.284), and error StD (0.280) values and the highest goodness-of-fit and prediction accuracies (0.84), was noted to yield the most reliable outcomes.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Zhou, J.W.; Cui, P.; Fang, H. Dynamic process analysis for the formation of Yangjiagou landslide-dammed lake triggered by the Wenchuan earthquake, China. Landslides
**2013**, 10, 331–342. [Google Scholar] [CrossRef] - Di Martire, D.; Tessitore, S.; Brancato, D.; Ciminelli, M.G.; Costabile, S.; Costantini, M.; Graziano, G.V.; Minati, F.; Ramondini, M.; Calcaterra, D. Landslide detection integrated system (LaDIS) based on in-situ and satellite SAR interferometry measurements. Catena
**2016**, 137, 406–421. [Google Scholar] [CrossRef] - Zhong, C.; Liu, Y.; Gao, P.; Chen, W.; Li, H.; Hou, Y.; Nuremanguli, T.; Ma, H. Landslide mapping with remote sensing: Challenges and opportunities. Int. J. Remote Sens.
**2020**, 41, 1555–1581. [Google Scholar] [CrossRef] - Pardeshi, S.D.; Autade, S.E.; Pardeshi, S.S. Landslide hazard assessment: Recent trends and techniques. SpringerPlus
**2013**, 2, 523. [Google Scholar] [CrossRef] [PubMed] - Bhunia, G.S.; Shit, P.K. Geospatial Technology for Multi-hazard Risk Assessment. In Geospatial Technology for Environmental Hazards: Modeling and Management in Asian Countries; Springer: Berlin/Heidelberg, Germany, 2022; pp. 1–18. [Google Scholar]
- Zhu, Y.; Yao, X.; Yao, L.; Yao, C. Detection and characterization of active landslides with multisource SAR data and remote sensing in western Guizhou, China. Natural Hazards
**2022**, 111, 973–994. [Google Scholar] [CrossRef] - Joyce, K.E.; Belliss, S.E.; Samsonov, S.V.; McNeill, S.J.; Glassey, P.J. A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Prog. Phys. Geogr.
**2009**, 33, 183–207. [Google Scholar] [CrossRef] - Yi, Y.; Zhang, W. A new deep-learning-based approach for earthquake-triggered landslide detection from single-temporal RapidEye satellite imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2020**, 13, 6166–6176. [Google Scholar] [CrossRef] - Lu, P.; Qin, Y.; Li, Z.; Mondini, A.C.; Casagli, N. Landslide mapping from multi-sensor data through improved change detection-based Markov random field. Remote Sens. Environ.
**2019**, 231, 111235. [Google Scholar] [CrossRef] - Ghorbanzadeh, O.; Blaschke, T.; Gholamnia, K.; Meena, S.R.; Tiede, D.; Aryal, J. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens.
**2019**, 11, 196. [Google Scholar] [CrossRef] - Liu, X.; Zhao, C.; Zhang, Q.; Lu, Z.; Li, Z.; Yang, C.; Zhu, W.; Liu-Zeng, J.; Chen, L.; Liu, C. Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China. Eng. Geol.
**2021**, 284, 106033. [Google Scholar] [CrossRef] - Ye, C.; Li, Y.; Cui, P.; Liang, L.; Pirasteh, S.; Marcato, J.; Goncalves, W.N.; Li, J. Landslide detection of hyperspectral remote sensing data based on deep learning with constrains. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2019**, 12, 5047–5060. [Google Scholar] [CrossRef] - Mondini, A.C.; Guzzetti, F.; Chang, K.-T.; Monserrat, O.; Martha, T.R.; Manconi, A. Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future. Earth-Sci. Rev.
**2021**, 216, 103574. [Google Scholar] [CrossRef] - Zhao, W.; Li, A.; Nan, X.; Zhang, Z.; Lei, G. Postearthquake landslides mapping from Landsat-8 data for the 2015 Nepal earthquake using a pixel-based change detection method. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2017**, 10, 1758–1768. [Google Scholar] [CrossRef] - Tzouvaras, M.; Danezis, C.; Hadjimitsis, D.G. Small scale landslide detection using Sentinel-1 interferometric SAR coherence. Remote Sens.
**2020**, 12, 1560. [Google Scholar] [CrossRef] - Chen, S.; Xiang, C.; Kang, Q.; Zhong, W.; Zhou, Y.; Liu, K. Accurate landslide detection leveraging UAV-based aerial remote sensing. IET Commun.
**2020**, 14, 2434–2441. [Google Scholar] [CrossRef] - Roback, K.; Clark, M.K.; West, A.J.; Zekkos, D.; Li, G.; Gallen, S.F.; Chamlagain, D.; Godt, J.W. The size, distribution, and mobility of landslides caused by the 2015 Mw7.8 Gorkha earthquake, Nepal. Geomorphology
**2018**, 301, 121–138. [Google Scholar] [CrossRef] - Danneels, G.; Pirard, E.; Havenith, H.-B. Automatic landslide detection from remote sensing images using supervised classification methods. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007; pp. 3014–3017. [Google Scholar]
- Tavakkoli Piralilou, S.; Shahabi, H.; Jarihani, B.; Ghorbanzadeh, O.; Blaschke, T.; Gholamnia, K.; Meena, S.R.; Aryal, J. Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas. Remote Sens.
**2019**, 11, 2575. [Google Scholar] [CrossRef] - Mohan, A.; Singh, A.K.; Kumar, B.; Dwivedi, R. Review on remote sensing methods for landslide detection using machine and deep learning. Trans. Emerg. Telecommun. Technol.
**2021**, 32, e3998. [Google Scholar] [CrossRef] - Dias, H.C.; Sandre, L.H.; Alarcón, D.A.S.; Grohmann, C.H.; Quintanilha, J.A. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil. Braz. J. Geol.
**2021**, 51, e20200105. [Google Scholar] [CrossRef] - Pradhan, B.; Jebur, M.N.; Shafri, H.Z.M.; Tehrany, M.S. Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and quickbird satellite imagery. IEEE Trans. Geosci. Remote Sens.
**2015**, 54, 1610–1622. [Google Scholar] [CrossRef] - Huang Lin, C.; Liu, D.; Liu, G. Landslide detection in La Paz City (Bolivia) based on time series analysis of InSAR data. Int. J. Remote Sens.
**2019**, 40, 6775–6795. [Google Scholar] [CrossRef] - Tehrani, F.S.; Calvello, M.; Liu, Z.; Zhang, L.; Lacasse, S. Machine learning and landslide studies: Recent advances and applications. Nat. Hazards
**2022**, 114, 1197–1245. [Google Scholar] [CrossRef] - Tien Bui, D.; Shahabi, H.; Shirzadi, A.; Chapi, K.; Alizadeh, M.; Chen, W.; Mohammadi, A.; Ahmad, B.B.; Panahi, M.; Hong, H. Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in cameron highlands, malaysia. Remote Sens.
**2018**, 10, 1527. [Google Scholar] [CrossRef] - Dou, J.; Yunus, A.P.; Bui, D.T.; Merghadi, A.; Sahana, M.; Zhu, Z.; Chen, C.-W.; Han, Z.; Pham, B.T. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides
**2020**, 17, 641–658. [Google Scholar] [CrossRef] - Nhu, V.-H.; Mohammadi, A.; Shahabi, H.; Ahmad, B.B.; Al-Ansari, N.; Shirzadi, A.; Geertsema, M.; Kress, V.R.; Karimzadeh, S.; Valizadeh Kamran, K. Landslide detection and susceptibility modeling on cameron highlands (Malaysia): A comparison between random forest, logistic regression and logistic model tree algorithms. Forests
**2020**, 11, 830. [Google Scholar] [CrossRef] - Ghorbanzadeh, O.; Xu, Y.; Ghamis, P.; Kopp, M.; Kreil, D. Landslide4sense: Reference benchmark data and deep learning models for landslide detection. arXiv
**2022**, arXiv:2206.00515. [Google Scholar] [CrossRef] - Kim, J.-M.; Yum, S.-G.; Park, H.; Bae, J. Strategic framework for natural disaster risk mitigation using deep learning and cost-benefit analysis. Nat. Hazards Earth Syst. Sci.
**2022**, 22, 2131–2144. [Google Scholar] [CrossRef] - LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature
**2015**, 521, 436–444. [Google Scholar] [CrossRef] - Baldi, P.; Sadowski, P.; Whiteson, D. Searching for exotic particles in high-energy physics with deep learning. Nat. Commun.
**2014**, 5, 4308. [Google Scholar] [CrossRef] - Cai, H.; Chen, T.; Niu, R.; Plaza, A. Landslide detection using densely connected convolutional networks and environmental conditions. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2021**, 14, 5235–5247. [Google Scholar] [CrossRef] - Ghorbanzadeh, O.; Crivellari, A.; Ghamisi, P.; Shahabi, H.; Blaschke, T. A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan). Sci. Rep.
**2021**, 11, 14629. [Google Scholar] [CrossRef] [PubMed] - Su, Z.; Chow, J.K.; Tan, P.S.; Wu, J.; Ho, Y.K.; Wang, Y.-H. Deep convolutional neural network–based pixel-wise landslide inventory mapping. Landslides
**2021**, 18, 1421–1443. [Google Scholar] [CrossRef] - Meena, S.R.; Soares, L.P.; Grohmann, C.H.; Van Westen, C.; Bhuyan, K.; Singh, R.P.; Floris, M.; Catani, F. Landslide detection in the Himalayas using machine learning algorithms and U-Net. Landslides
**2022**, 19, 1209–1229. [Google Scholar] [CrossRef] - Althuwaynee, O.F.; Pradhan, B.; Park, H.-J.; Lee, J.H. A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides
**2014**, 11, 1063–1078. [Google Scholar] [CrossRef] - Huang, Y.; Zhao, L. Review on landslide susceptibility mapping using support vector machines. Catena
**2018**, 165, 520–529. [Google Scholar] [CrossRef] - Umar, Z.; Pradhan, B.; Ahmad, A.; Jebur, M.N.; Tehrany, M.S. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena
**2014**, 118, 124–135. [Google Scholar] [CrossRef] - Thai Pham, B.; Tien Bui, D.; Prakash, I. Landslide susceptibility modelling using different advanced decision trees methods. Civ. Eng. Environ. Syst.
**2018**, 35, 139–157. [Google Scholar] [CrossRef] - Merghadi, A.; Yunus, A.P.; Dou, J.; Whiteley, J.; ThaiPham, B.; Bui, D.T.; Avtar, R.; Abderrahmane, B. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Sci. Rev.
**2020**, 207, 103225. [Google Scholar] [CrossRef] - Reichenbach, P.; Rossi, M.; Malamud, B.D.; Mihir, M.; Guzzetti, F. A review of statistically-based landslide susceptibility models. Earth-Sci. Rev.
**2018**, 180, 60–91. [Google Scholar] [CrossRef] - Shano, L.; Raghuvanshi, T.K.; Meten, M. Landslide susceptibility evaluation and hazard zonation techniques—A review. Geoenviron. Disasters
**2020**, 7, 18. [Google Scholar] [CrossRef] - Taalab, K.; Cheng, T.; Zhang, Y. Mapping landslide susceptibility and types using Random Forest. Big Earth Data
**2018**, 2, 159–178. [Google Scholar] [CrossRef] - Lombardo, L.; Mai, P.M. Presenting logistic regression-based landslide susceptibility results. Eng. Geol.
**2018**, 244, 14–24. [Google Scholar] [CrossRef] - Dou, J.; Yamagishi, H.; Pourghasemi, H.R.; Yunus, A.P.; Song, X.; Xu, Y.; Zhu, Z. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat. Hazards
**2015**, 78, 1749–1776. [Google Scholar] [CrossRef] - Shafapourtehrany, M. Exploring the Risky Areas Due to Landslide Using Decision Tree Analysis: Case Study Tasmania, Australia. Eurasia Proc. Sci. Technol. Eng. Math.
**2022**, 18, 86–101. [Google Scholar] [CrossRef] - Abu El-Magd, S.A.; Ali, S.A.; Pham, Q.B. Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Sci. Inform.
**2021**, 14, 1227–1243. [Google Scholar] [CrossRef] - Armaş, I. Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania. Nat. Hazards
**2012**, 60, 937–950. [Google Scholar] [CrossRef] - Paryani, S.; Neshat, A.; Javadi, S.; Pradhan, B. Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping. Nat. Hazards
**2020**, 103, 1961–1988. [Google Scholar] [CrossRef] - Ding, Q.; Chen, W.; Hong, H. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int.
**2017**, 32, 619–639. [Google Scholar] [CrossRef] - Jaboyedoff, M.; Oppikofer, T.; Derron, M.-H.; Blikra, L.H.; Böhme, M.; Saintot, A. Complex landslide behaviour and structural control: A three-dimensional conceptual model of Åknes rockslide, Norway. Geol. Soc. Lond. Spec. Publ.
**2011**, 351, 147–161. [Google Scholar] [CrossRef] - Fang, Z.; Wang, Y.; Peng, L.; Hong, H. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int. J. Geogr. Inf. Sci.
**2021**, 35, 321–347. [Google Scholar] [CrossRef] - Pham, B.T.; Jaafari, A.; Prakash, I.; Bui, D.T. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull. Eng. Geol. Environ.
**2019**, 78, 2865–2886. [Google Scholar] [CrossRef] - Kalantar, B.; Ueda, N.; Saeidi, V.; Ahmadi, K.; Halin, A.A.; Shabani, F. Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data. Remote Sens.
**2020**, 12, 1737. [Google Scholar] [CrossRef] - Hakim, W.L.; Rezaie, F.; Nur, A.S.; Panahi, M.; Khosravi, K.; Lee, C.-W.; Lee, S. Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea. J. Environ. Manag.
**2022**, 305, 114367. [Google Scholar] [CrossRef] [PubMed] - Xu, Q.; Shi, Y.; Guo, J.; Ouyang, C.; Zhu, X.X. UCDFormer: Unsupervised change detection using a transformer-driven image translation. arXiv
**2023**, arXiv:2308.01146. [Google Scholar] - Ramos-Bernal, R.N.; Vázquez-Jiménez, R.; Cantú-Ramírez, C.A.; Alarcón-Paredes, A.; Alonso-Silverio, G.A.; Bruzón, A.G.; Arrogante-Funes, F.; Martín-González, F.; Novillo, C.J.; Arrogante-Funes, P. Evaluation of conditioning factors of slope instability and continuous change maps in the generation of landslide inventory maps using machine learning (ML) algorithms. Remote Sens.
**2021**, 13, 4515. [Google Scholar] [CrossRef] - Sabokbar, H.F.; Roodposhti, M.S.; Tazik, E. Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology
**2014**, 226, 15–24. [Google Scholar] [CrossRef] - Dou, J.; Chang, K.-T.; Chen, S.; Yunus, A.P.; Liu, J.-K.; Xia, H.; Zhu, Z. Automatic case-based reasoning approach for landslide detection: Integration of object-oriented image analysis and a genetic algorithm. Remote Sens.
**2015**, 7, 4318–4342. [Google Scholar] [CrossRef] - Kong, L.; Cheng, J. Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion. Biomed. Signal Process. Control
**2022**, 77, 103772. [Google Scholar] [CrossRef] - Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. Proceedings of Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015; Proceedings, Part III 18. pp. 234–241. [Google Scholar]
- Sharma, S.; Guleria, K.; Tiwari, S.; Kumar, S. A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans. Meas. Sens.
**2022**, 24, 100506. [Google Scholar] [CrossRef] - Zhou, J.; Yang, X.; Zhang, L.; Shao, S.; Bian, G. Multisignal VGG19 network with transposed convolution for rotating machinery fault diagnosis based on deep transfer learning. Shock Vib.
**2020**, 2020, 8863388. [Google Scholar] [CrossRef] - Ghalambaz, M.; Yengejeh, R.J.; Davami, A.H. Building energy optimization using grey wolf optimizer (GWO). Case Stud. Therm. Eng.
**2021**, 27, 101250. [Google Scholar] [CrossRef] - Hosseini, S.; Al Khaled, A. A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research. Appl. Soft Comput.
**2014**, 24, 1078–1094. [Google Scholar] [CrossRef] - Karimzadeh, S.; Matsuoka, M. A weighted overlay method for liquefaction-related urban damage detection: A case study of the 6 September 2018 Hokkaido Eastern Iburi earthquake, Japan. Geosciences
**2018**, 8, 487. [Google Scholar] [CrossRef] - Arimura, M.; Ha, T.V.; Kimura, N.; Asada, T. Evacuation awareness and behavior in the event of a tsunami in an aging society: An experience from the 2018 Hokkaido Eastern Iburi earthquake. Saf. Sci.
**2020**, 131, 104906. [Google Scholar] [CrossRef] - JMA. Technical Description-2 Magnitude Determination; Japan Meteorological Agency: Tokyo, Japan, 2019.
- Zhang, S.; Li, R.; Wang, F.; Iio, A. Characteristics of landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake, Northern Japan. Landslides
**2019**, 16, 1691–1708. [Google Scholar] [CrossRef] - Osanai, N.; Yamada, T.; Hayashi, S.-I.; Kastura, S.Y.; Furuichi, T.; Yanai, S.; Murakami, Y.; Miyazaki, T.; Tanioka, Y.; Takiguchi, S. Characteristics of landslides caused by the 2018 Hokkaido Eastern Iburi Earthquake. Landslides
**2019**, 16, 1517–1528. [Google Scholar] [CrossRef] - Yamagishi, H.; Yamazaki, F. Landslides by the 2018 Hokkaido Iburi-Tobu Earthquake on September 6. Landslides
**2018**, 15, 2521–2524. [Google Scholar] [CrossRef] - Kawamura, S.; Kawajiri, S.; Hirose, W.; Watanabe, T. Slope failures/landslides over a wide area in the 2018 Hokkaido Eastern Iburi earthquake. Soils Found.
**2019**, 59, 2376–2395. [Google Scholar] [CrossRef] - Ito, Y.; Yamazaki, S.; Kurahashi, T. Geological features of landslides caused by the 2018 Hokkaido Eastern Iburi Earthquake in Japan. Geol. Soc. Lond. Spec. Publ.
**2021**, 501, 171–183. [Google Scholar] [CrossRef] - Foumelis, M.; Blasco, J.M.D.; Desnos, Y.-L.; Engdahl, M.; Fernández, D.; Veci, L.; Lu, J.; Wong, C. ESA SNAP-StaMPS integrated processing for Sentinel-1 persistent scatterer interferometry. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 1364–1367. [Google Scholar]
- Meena, S.R.; Ghorbanzadeh, O.; Blaschke, T. A comparative study of statistics-based landslide susceptibility models: A case study of the region affected by the gorkha earthquake in nepal. ISPRS Int. J. Geo-Inf.
**2019**, 8, 94. [Google Scholar] [CrossRef] - Pawluszek, K.; Borkowski, A. Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Nat. Hazards
**2017**, 86, 919–952. [Google Scholar] [CrossRef] - Batar, A.K.; Watanabe, T. Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the Indian Himalayan Region: Recent developments, gaps, and future directions. ISPRS Int. J. Geo-Inf.
**2021**, 10, 114. [Google Scholar] [CrossRef] - Ali, S.A.; Parvin, F.; Vojteková, J.; Costache, R.; Linh, N.T.T.; Pham, Q.B.; Vojtek, M.; Gigović, L.; Ahmad, A.; Ghorbani, M.A. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms. Geosci. Front.
**2021**, 12, 857–876. [Google Scholar] [CrossRef] - Liu, L.L.; Yang, C.; Huang, F.M.; Wang, X.M. Landslide susceptibility mapping by attentional factorization machines considering feature interactions. Geomat. Nat. Hazards Risk
**2021**, 12, 1837–1861. [Google Scholar] [CrossRef] - Mahalingam, R.; Olsen, M.J.; O’Banion, M.S. Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study). Geomat. Nat. Hazards Risk
**2016**, 7, 1884–1907. [Google Scholar] [CrossRef] - Ayalew, L.; Yamagishi, H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology
**2005**, 65, 15–31. [Google Scholar] [CrossRef] - Zhou, C.; Yin, K.; Cao, Y.; Ahmed, B.; Li, Y.; Catani, F.; Pourghasemi, H.R. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Comput. Geosci.
**2018**, 112, 23–37. [Google Scholar] [CrossRef] - Pradhan, B.; Lee, S. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides
**2010**, 7, 13–30. [Google Scholar] [CrossRef] - Ohlmacher, G.C. Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng. Geol.
**2007**, 91, 117–134. [Google Scholar] [CrossRef] - Lee, S.; Lee, M.-J.; Jung, H.-S. Data mining approaches for landslide susceptibility mapping in Umyeonsan, Seoul, South Korea. Appl. Sci.
**2017**, 7, 683. [Google Scholar] [CrossRef] - Nguyen, V.V.; Pham, B.T.; Vu, B.T.; Prakash, I.; Jha, S.; Shahabi, H.; Shirzadi, A.; Ba, D.N.; Kumar, R.; Chatterjee, J.M. Hybrid machine learning approaches for landslide susceptibility modeling. Forests
**2019**, 10, 157. [Google Scholar] [CrossRef] - Pham, B.T.; Khosravi, K.; Prakash, I. Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area, Uttarakhand, India. Environ. Process.
**2017**, 4, 711–730. [Google Scholar] [CrossRef] - Nicu, I.C.; Asăndulesei, A. GIS-based evaluation of diagnostic areas in landslide susceptibility analysis of Bahluieț River Basin (Moldavian Plateau, NE Romania). Are Neolithic sites in danger? Geomorphology
**2018**, 314, 27–41. [Google Scholar] [CrossRef] - Farrokhnia, A.; Pirasteh, S.; Pradhan, B.; Pourkermani, M.; Arian, M. A recent scenario of mass wasting and its impact on the transportation in Alborz Mountains, Iran using geo-information technology. Arab. J. Geosci.
**2011**, 4, 1337–1349. [Google Scholar] [CrossRef] - Li, Y.; Chen, G.; Tang, C.; Zhou, G.; Zheng, L. Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network. Nat. Hazards Earth Syst. Sci.
**2012**, 12, 2719–2729. [Google Scholar] [CrossRef] - Huang, F.; Pan, L.; Fan, X.; Jiang, S.-H.; Huang, J.; Zhou, C. The uncertainty of landslide susceptibility prediction modeling: Suitability of linear conditioning factors. Bull. Eng. Geol. Environ.
**2022**, 81, 182. [Google Scholar] [CrossRef] - Yu, H.; Ma, Y.; Wang, L.; Zhai, Y.; Wang, X. A landslide intelligent detection method based on CNN and RSG_R. In Proceedings of the 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, 6–9 August 2017; pp. 40–44. [Google Scholar]
- Hacıefendioğlu, K.; Başağa, H.B.; Yavuz, Z.; Karimi, M.T. Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method. Renew. Energy
**2022**, 182, 1–16. [Google Scholar] [CrossRef] - Wagh, V.K. A Hybrid Model of Sectorization & Evacuation Path Detection for Disaster Affected Areas; National College of Ireland: Dublin, Ireland, 2020. [Google Scholar]
- Cao, Z.; Huang, J.; He, X.; Zong, Z. BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images. Knowl.-Based Syst.
**2022**, 258, 110040. [Google Scholar] [CrossRef] - Ahmed, I.; Ahmad, M.; Jeon, G. A real-time efficient object segmentation system based on U-Net using aerial drone images. J. Real-Time Image Process.
**2021**, 18, 1745–1758. [Google Scholar] [CrossRef] - Bonet, I.; Caraffini, F.; Pena, A.; Puerta, A.; Gongora, M. Oil palm detection via deep transfer learning. In Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 19–24 July 2020; pp. 1–8. [Google Scholar]
- Li, C.; Yi, B.; Gao, P.; Li, H.; Sun, J.; Chen, X.; Zhong, C. Valuable clues for DCNN-based landslide detection from a comparative assessment in the Wenchuan earthquake area. Sensors
**2021**, 21, 5191. [Google Scholar] [CrossRef] - Ajit, A.; Acharya, K.; Samanta, A. A review of convolutional neural networks. In Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 24–25 February 2020; pp. 1–5. [Google Scholar]
- Fofana, T.; Ouattara, S.; Clement, A. Optimal Flame Detection of Fires in Videos Based on Deep Learning and the Use of Various Optimizers. Open J. Appl. Sci.
**2021**, 11, 1240–1255. [Google Scholar] [CrossRef] - Hakim, W.L.; Lee, C.-W. A review on remote sensing and GIS applications to monitor natural disasters in Indonesia. Korean J. Remote Sens.
**2020**, 36, 1303–1322. [Google Scholar] - Wang, Y.; Fang, Z.; Hong, H. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci. Total Environ.
**2019**, 666, 975–993. [Google Scholar] [CrossRef] [PubMed] - Gu, J.; Wang, Z.; Kuen, J.; Ma, L.; Shahroudy, A.; Shuai, B.; Liu, T.; Wang, X.; Wang, G.; Cai, J. Recent advances in convolutional neural networks. Pattern Recognit.
**2018**, 77, 354–377. [Google Scholar] [CrossRef] - Ngo, P.T.T.; Panahi, M.; Khosravi, K.; Ghorbanzadeh, O.; Kariminejad, N.; Cerda, A.; Lee, S. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci. Front.
**2021**, 12, 505–519. [Google Scholar] - Zafar, A.; Aamir, M.; Nawi, N.M.; Arshad, A.; Riaz, S.; Alruban, A.; Dutta, A.K.; Almotairi, S. A Comparison of Pooling Methods for Convolutional Neural Networks. Appl. Sci.
**2022**, 12, 8643. [Google Scholar] [CrossRef] - Khosravi, K.; Panahi, M.; Golkarian, A.; Keesstra, S.D.; Saco, P.M.; Bui, D.T.; Lee, S. Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran. J. Hydrol.
**2020**, 591, 125552. [Google Scholar] [CrossRef] - Li, M.; Jia, D.; Wu, Z.; Qiu, S.; He, W. Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN). Mech. Syst. Signal Process.
**2022**, 165, 108289. [Google Scholar] [CrossRef] - Kattenborn, T.; Leitloff, J.; Schiefer, F.; Hinz, S. Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J. Photogramm. Remote Sens.
**2021**, 173, 24–49. [Google Scholar] [CrossRef] - Xu, X.; Ma, F.; Zhou, J.; Du, C. Applying convolutional neural networks (CNN) for end-to-end soil analysis based on laser-induced breakdown spectroscopy (LIBS) with less spectral preprocessing. Comput. Electron. Agric.
**2022**, 199, 107171. [Google Scholar] [CrossRef] - Tien Bui, D.; Shahabi, H.; Shirzadi, A.; Chapi, K.; Hoang, N.-D.; Pham, B.T.; Bui, Q.-T.; Tran, C.-T.; Panahi, M.; Bin Ahmad, B. A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides. Remote Sens.
**2018**, 10, 1538. [Google Scholar] [CrossRef] - Abdollahi, M.; Isazadeh, A.; Abdollahi, D. Imperialist competitive algorithm for solving systems of nonlinear equations. Comput. Math. Appl.
**2013**, 65, 1894–1908. [Google Scholar] [CrossRef] - Barkhoda, W.; Sheikhi, H. Immigrant imperialist competitive algorithm to solve the multi-constraint node placement problem in target-based wireless sensor networks. Ad Hoc Netw.
**2020**, 106, 102183. [Google Scholar] [CrossRef] - Song, X.; Tang, L.; Zhao, S.; Zhang, X.; Li, L.; Huang, J.; Cai, W. Grey wolf optimizer for parameter estimation in surface waves. Soil Dyn. Earthq. Eng.
**2015**, 75, 147–157. [Google Scholar] [CrossRef] - Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey wolf optimizer. Adv. Eng. Softw.
**2014**, 69, 46–61. [Google Scholar] [CrossRef] - Chen, W.; Hong, H.; Panahi, M.; Shahabi, H.; Wang, Y.; Shirzadi, A.; Pirasteh, S.; Alesheikh, A.A.; Khosravi, K.; Panahi, S. Spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo). Appl. Sci.
**2019**, 9, 3755. [Google Scholar] [CrossRef] - Jaafari, A.; Panahi, M.; Pham, B.T.; Shahabi, H.; Bui, D.T.; Rezaie, F.; Lee, S. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena
**2019**, 175, 430–445. [Google Scholar] [CrossRef] - Soares, L.P.; Dias, H.C.; Grohmann, C.H. Landslide segmentation with U-Net: Evaluating different sampling methods and patch sizes. arXiv
**2020**, arXiv:2007.06672. [Google Scholar] - Wang, H.; Zhang, L.; Yin, K.; Luo, H.; Li, J. Landslide identification using machine learning. Geosci. Front.
**2021**, 12, 351–364. [Google Scholar] [CrossRef] - Lei, T.; Zhang, Y.; Lv, Z.; Li, S.; Liu, S.; Nandi, A.K. Landslide inventory mapping from bitemporal images using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett.
**2019**, 16, 982–986. [Google Scholar] [CrossRef] - Liu, P.; Wei, Y.; Wang, Q.; Chen, Y.; Xie, J. Research on post-earthquake landslide extraction algorithm based on improved U-Net model. Remote Sens.
**2020**, 12, 894. [Google Scholar] [CrossRef] - Abedini, M.; Ghasemian, B.; Shirzadi, A.; Shahabi, H.; Chapi, K.; Pham, B.T.; Bin Ahmad, B.; Tien Bui, D. A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int.
**2019**, 34, 1427–1457. [Google Scholar] [CrossRef] - Lin, Q.; Lima, P.; Steger, S.; Glade, T.; Jiang, T.; Zhang, J.; Liu, T.; Wang, Y. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data. Geosci. Front.
**2021**, 12, 101248. [Google Scholar] [CrossRef] - Kadirhodjaev, A.; Rezaie, F.; Lee, M.-J.; Lee, S. Landslide susceptibility assessment using an optimized group method of data handling model. ISPRS Int. J. Geo-Inf.
**2020**, 9, 566. [Google Scholar] [CrossRef] - Lee, S.; Sambath, T. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ. Geol.
**2006**, 50, 847–855. [Google Scholar] [CrossRef] - Poudyal, C.P.; Chang, C.; Oh, H.-J.; Lee, S. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya. Environ. Earth Sci.
**2010**, 61, 1049–1064. [Google Scholar] [CrossRef] - Yilmaz, I. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Comput. Geosci.
**2009**, 35, 1125–1138. [Google Scholar] [CrossRef] - Nanni, L.; Ghidoni, S.; Brahnam, S. Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recognit.
**2017**, 71, 158–172. [Google Scholar] [CrossRef] - Fan, L.; Zhang, F.; Fan, H.; Zhang, C. Brief review of image denoising techniques. Vis. Comput. Ind. Biomed. Art
**2019**, 2, 7. [Google Scholar] [CrossRef] - Mahony, N.; Campbell, S.; Carvalho, A.; Harapanahalli, S.; Velasco-Hernandez, G.; Krpalkova, L.; Riordan, D.; Walsh, J. Deep learning vs. Traditional computer vision. arXiv
**2020**, arXiv:1910.13796. [Google Scholar] - Blondeau-Patissier, D.; Schroeder, T.; Suresh, G.; Li, Z.; Diakogiannis, F.I.; Irving, P.; Witte, C.; Steven, A.D. Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park. Mar. Pollut. Bull.
**2023**, 188, 114598. [Google Scholar] [CrossRef] [PubMed] - Ghosh, B.; Garg, S.; Motagh, M. Automatic Flood Detection from SENTINEL-1 Data Using Deep Learning Architectures. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.
**2022**, 3, 201–208. [Google Scholar] [CrossRef] - Li, Y.; Martinis, S.; Wieland, M. Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence. ISPRS J. Photogramm. Remote Sens.
**2019**, 152, 178–191. [Google Scholar] [CrossRef] - Iman, M.; Arabnia, H.R.; Rasheed, K. A review of deep transfer learning and recent advancements. Technologies
**2023**, 11, 40. [Google Scholar] [CrossRef] - Shao, Z.; Zhou, W.; Deng, X.; Zhang, M.; Cheng, Q. Multilabel remote sensing image retrieval based on fully convolutional network. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2020**, 13, 318–328. [Google Scholar] [CrossRef] - Qin, S.; Guo, X.; Sun, J.; Qiao, S.; Zhang, L.; Yao, J.; Cheng, Q.; Zhang, Y. Landslide detection from open satellite imagery using distant domain transfer learning. Remote Sens.
**2021**, 13, 3383. [Google Scholar] [CrossRef] - Hou, Y.; Gao, H.; Wang, Z.; Du, C. Improved grey wolf optimization algorithm and application. Sensors
**2022**, 22, 3810. [Google Scholar] [CrossRef] [PubMed] - Nosratabadi, S.; Szell, K.; Beszedes, B.; Imre, F.; Ardabili, S.; Mosavi, A. Comparative analysis of ANN-ICA and ANN-GWO for crop yield prediction. In Proceedings of the 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh city, Vietnam, 14-15 October 2020; pp. 1–5. [Google Scholar]
- Nur, A.S.; Kim, Y.J.; Lee, C.-W. Creation of wildfire susceptibility maps in plumas national forest using InSAR coherence, deep learning, and metaheuristic optimization approaches. Remote Sens.
**2022**, 14, 4416. [Google Scholar] [CrossRef] - Jaafari, A.; Najafi, A.; Rezaeian, J.; Sattarian, A.; Ghajar, I. Planning road networks in landslide-prone areas: A case study from the northern forests of Iran. Land Use Policy
**2015**, 47, 198–208. [Google Scholar] [CrossRef] - Nsengiyumva, J.B.; Valentino, R. Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment. Geomat. Nat. Hazards Risk
**2020**, 11, 1250–1277. [Google Scholar] [CrossRef] - Pham, B.T.; Van Phong, T.; Nguyen-Thoi, T.; Trinh, P.T.; Tran, Q.C.; Ho, L.S.; Singh, S.K.; Duyen, T.T.T.; Nguyen, L.T.; Le, H.Q. GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv. Space Res.
**2020**, 66, 1303–1320. [Google Scholar] [CrossRef]

**Figure 1.**Process flow of the research, representing the model development for landslide detection and susceptibility mapping.

**Figure 2.**Study area: (

**a**) location of Japan capital (black star symbol) and Iburi region of Hokkaido, Northern Japan (red square symbol), (

**b**) The digital elevation map, and (

**c**) location of landslides occurred during and after the 2018 Iburi earthquake (white symbol).

**Figure 4.**Spatial database of landslide susceptibility-related factors: (

**a**) elevation, (

**b**) slope, (

**c**) plan curvature, (

**d**) valley depth, (

**e**) TWI, (

**f**) land cover, (

**g**) rainfall, (

**h**) distance to rivers, and (

**i**) distance to roads.

**Figure 9.**Final segmentation results derived from (

**a**) U-Net, (

**b**) VGG-16, and (

**c**) VGG-19 (The black box shows the difference in predictive ability between the models and white box is the zoomed in view of the black box).

**Figure 10.**Performance evaluation of U-Net, VGG-16, and VGG-19 in the (

**a**) training and (

**b**) test steps.

**Figure 12.**Earthquake-induced landslide susceptibility maps created by (

**a**) CNN, (

**b**) CNN-ICA, and (

**c**) CNN-GWO.

**Figure 13.**Proportion of areas of different classes in susceptibility maps derived from CNN, CNN-ICA, and CNN-GWO.

**Figure 14.**Analysis of errors of the landslide susceptibility models (i.e., (

**a**–

**d**) CNN, (

**e**–

**h**) CNN-ICA, and (

**i**–

**l**) CNN-GWO) using the training (

**left side**) and testing (

**right side**) datasets.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Shafapourtehrany, M.; Rezaie, F.; Jun, C.; Heggy, E.; Bateni, S.M.; Panahi, M.; Özener, H.; Shabani, F.; Moeini, H.
Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms. *Remote Sens.* **2023**, *15*, 4501.
https://doi.org/10.3390/rs15184501

**AMA Style**

Shafapourtehrany M, Rezaie F, Jun C, Heggy E, Bateni SM, Panahi M, Özener H, Shabani F, Moeini H.
Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms. *Remote Sensing*. 2023; 15(18):4501.
https://doi.org/10.3390/rs15184501

**Chicago/Turabian Style**

Shafapourtehrany, Mahyat, Fatemeh Rezaie, Changhyun Jun, Essam Heggy, Sayed M. Bateni, Mahdi Panahi, Haluk Özener, Farzin Shabani, and Hamidreza Moeini.
2023. "Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms" *Remote Sensing* 15, no. 18: 4501.
https://doi.org/10.3390/rs15184501