Landslide Susceptibility Mapping with Deep Learning Algorithms

Habumugisha, Jules Maurice; Chen, Ningsheng; Rahman, Mahfuzur; Islam, Monirul; Ahmad, Hilal; Elbeltagi, Ahmed; Sharma, Gitika; Liza, Sharmina Naznin; Dewan, Ashraf

doi:10.3390/su14031734

Cited by 76 publications

(30 citation statements)

References 85 publications

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“…DNN is a popular method employed in various natural hazard-related studies. It consists of several fully connected layers, dropout layers, hyperparameters for random search, activation function, and optimization algorithm [157]. RNN is another DL model with great success in the temporal data processing.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

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Section: Deep Learning Methodsmentioning

confidence: 99%

“…Habumugisha et al [157] explored four DL methods, namely CNN, DNN, LSTM, and RNN. The study used one-dimensional architecture for the CNN model, which consisted of one convolution layer, one pooling layer, one flattening layer, and two fully connected dense layers.…”

Section: Literature On Deep Learning Methodsmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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“…Another study in Maoxian County, Sichuan, China, applied four deep learning algorithms; namely, (1) the convolutional neural network (CNN), (2) deep neural network (DNN), (3) long short-term memory (LSTM) networks, and (4) recurrent neural network (RNN), were used to assess the risk of landslides (Habumugisha et al 2022). With historical records, field observations, and remote sensing techniques, a total of 1290 landslide records were created.…”

Section: Deep Learning In Landslide Susceptibility Mappingmentioning

confidence: 99%

Deep Learning in Landslide Studies: A Review

Hemalatha

2023

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

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This study reviews deep learning architectures and techniques used in the landslide domain. This study aims to understand the state of the art, challenges, and opportunities of applying deep learning to landslide research. Every paper discussed in this article is reviewed for the deep learning approach employed, the study area where it is implemented, additional benchmark algorithms implemented, model assessment metrics, the best model that is selected, and the limitations mentioned by the authors. This review increases visibility into (1) various deep learning methodologies as implemented in real-world landslide mapping, detection, monitoring, and prediction case studies, (2) projects constraints of applying deep learning to landslide research (3) provides recommendations and breakthroughs that must be established in certain areas of landslide studies.

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“…Landslides can occur as a result of natural characteristics that influence slope stability, as well as subsequent and triggering factors that can be caused by either natural or anthropogenic factors [1]. There appear to be significant regional and global impacts, such as decreased agricultural productivity, increased soil eroding, habitat destruction and death tolls, and infrastructure damage due to landslides [2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Framework for Landslide Severity Prediction and Susceptibility Mapping

Bhargavi¹,

Arunnehru²

2023

Intelligent Automation &Amp; Soft Computing

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Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala's Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and distance from road, lithology, and rainfall are considered. A landslide susceptibility map was generated using the Arc geographic information system (GIS) tool for all the triggering factors using frequency ratio method, Shannon entropy method, Relative effect method, and fuzzy logic method. A new Deep Neural Network (DNN) framework has been developed for the multiclass classification and prediction of landslide hazard zones as low, moderate, high, and very high. Existing works are only uses statistical methods, but the proposed work has used DNN to predict landslide severity at four different level even for semi data with over accurately. The training data for deep learning model are generated using the Sentinel Satellite images and field survey. The label for training the data are generated from the Landslide Susceptibility Index which are generated from statistical method. Among all the statistical method generated data the Shannon Entropy data is the most accurate of the four statistical methods achieves 99.16%, accuracy. The frequency ratio method based data achieves 97.08% accuracy, the relative effect method based data achieves 92.72% accuracy, and the Fuzzy logic method based data achieves 86.60% accuracy.

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Landslide Susceptibility Mapping with Deep Learning Algorithms

Cited by 76 publications

References 85 publications

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Deep Learning in Landslide Studies: A Review

Deep Learning Framework for Landslide Severity Prediction and Susceptibility Mapping

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