# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 5 Conclusions  
In the present study, a methodological approach was introduced to develop a DNN model, a kernel-based DNN that combines a kernel with the DNN model, and a CNN model, all of which were trained based on a SGD. The location data for training the susceptibility of a landslide was constructed by analyzing aerial photographs and field survey results. Topographic variables were extracted from aerial photographs and digital topographic maps, and soil and forest influencing variables were also extracted. A landslide inventory was constructed using the training dataset built through oversampling for the landslide area and non-landslide area, with the landslide influencing variables; modeling was performed for three models based on the inventory. Finally, the resulting map was evaluated using a test set (20%) of landslide data that was not used for training. The training and prediction functions of the developed DL model were evaluated and compared with the results of RMSE estimation and AP score analysis. The experimental results showed that the optimized DNN model had the highest predictive power (99.45%), followed by the kernel-based DNN (99.44%) and CNN model (99.41%) methods. The recall and precision of all models showed sufficiently high results, and the performance of all models was more than 99% satisfactory.  
The expected landslide susceptibility was high near the top of the mountain and was similar to the existing landslide pattern. The susceptibility of landslides decreases according to the slope of the susceptibility map of this study, which is a logical result of the occurrence of debris flow due to the shape of the topography. Nevertheless, there are some limitations to the implementation of neural network (NN)-based DL models. In selecting variables, it is necessary to apply a different methodology in advance, in order to understand the fundamental relationship between variables and landslides, just as variables were selected based on previous studies in this study. Also, the model architecture is less intuitive due to the black box of the NN model. That means selecting the right hyperparameters is a task that requires experiential expertise.  
Compared to water-related disasters, landslides were not previously considered a serious problem in urban areas. Most cities developed rapidly without taking into account the damage caused by natural disasters in the process of urbanization. However, due to the recent increase in local heavy rain, extreme precipitation and population pressure in any part of the city can have serious consequences. Therefore, it is important to establish basic scientific data to prevent landslides in urban areas such as Seoul. Regarding the applicability of the developed methodology, the results of this study are expected to support decision-making to identify spatial problems related to urban landslides and effective risk reduction policies.  
**Author Contributions**: [PERSON]. ([PERSON]), [PERSON], and [PERSON] ([PERSON]) collected data. [PERSON] and [PERSON] processed input data. [PERSON]. ([PERSON]) and [PERSON] managed the paperwork and interpreted the results. [PERSON] suggested the idea and prepared the data. All of the authors contributed to the writing of each part. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This work was supported by the National Research Foundation of Korea, funded by the Korean government under grant NRF-2018M1A3A3A02066008. This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) and Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT. This research was supported by a grant (20010017) of R&D Program of Responding Technology to Climate Disaster such as Heat Wave funded by Ministry of Interior and Safety (MOIS, Korea) which was conducted by the Korea Environment Institute (KEI).  
**Conflicts of Interest**: The authors declare no conflict of interest.