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Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

WATER (2023)

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Article Engineering, Environmental

A novel swarm intelligence: cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment

Rana Muhammad Adnan Ikram et al.

Summary: A landslide susceptibility map is crucial for minimizing damages caused by landslides. This study introduces a novel approach using the cuckoo optimization algorithm (COA) and the SailFish optimizer (SFO) to develop an artificial neural network (ANN) for landslide forecasting. The hybrid model of COA-MLP showed the best performance in landslide detection and can be useful for planners in identifying dangerous locations.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2023)

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Article Environmental Sciences

A Combination of Deep Autoencoder and Multi-Scale Residual Network for Landslide Susceptibility Evaluation

Zhuolu Wang et al.

Summary: This paper proposes a new evaluation method for landslide susceptibility combining deep autoencoder and multi-scale residual network. The method can accurately predict the spatial distribution of potential landslides, which is of great practical importance for disaster prevention, disaster reduction, and land resource management.

REMOTE SENSING (2023)

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Article Environmental Sciences

Comparisons of Convolutional Neural Network and Other Machine Learning Methods in Landslide Susceptibility Assessment: A Case Study in Pingwu

Ziyu Jiang et al.

Summary: This study compares the characteristics and effectiveness of convolutional neural network (CNN) and conventional machine learning (ML) methods in landslide susceptibility assessment (LSA). The result shows that CNN achieves the highest accuracy (86.41%) and the highest AUC (0.9249) in LSA. The CNN model performs better in identifying landslide cluster regions due to its consideration of surrounding environment information through convolution operation. The high accuracy and concentrated possibility of CNN in LSA is of great significance for disaster prevention and mitigation, although there is room for improvement in identifying low-cluster landslide areas.

REMOTE SENSING (2023)

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Review Geosciences, Multidisciplinary

A critical review on landslide susceptibility zonation: recent trends, techniques, and practices in Indian Himalaya

Suvam Das et al.

Summary: The Indian Himalayan region is prone to landslides due to its complex geology, rugged topography, steep slopes, seismic activity, and heavy rainfall. Landslide susceptibility mapping is an effective solution for estimating vulnerability and determining potential consequences. This paper reviews 144 research articles published from 2010 to 2020 to understand the recent trends, techniques, and practices in landslide susceptibility zoning in the region. The review provides valuable insights for global practitioners and serves as a relevant database for scientists and researchers in the field of landslides, particularly in the Himalayan region.

NATURAL HAZARDS (2023)

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Article Geosciences, Multidisciplinary

Landslide susceptibility assessment using the certainty factor and deep neural network

Wenli Ma et al.

Summary: Areas with vulnerable ecological environments often experience geological disasters such as landslides, posing a severe threat to lives and property. This study proposes a combined approach using the coefficient of determination method (CF) and a deep neural network (DNN) for landslide susceptibility evaluation. The DNN excavates deep features of samples and improves accuracy, while logistic regression (LRM) and support vector machine (SVM) methods are used for comparison. Based on remote sensing interpretation and field investigations, a spatial database is established, and eight factors, including elevation and slope, are selected for evaluation. The results reveal significant influences of ground elevation, curvature, distance from fault, and distance from road on landslide susceptibility. The CF-DNN method is found to be more suitable, resulting in a landslide susceptibility zoning with four sensitivity levels.

FRONTIERS IN EARTH SCIENCE (2023)

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Article Geosciences, Multidisciplinary

Comparative analysis of multiple conventional neural networks for landslide susceptibility mapping

Bilal Aslam et al.

Summary: This study assesses and researches the landslide susceptibility in northern Pakistan using various convolutional neural network (CNN) architectures and residual networks (ResNet). The results show that the CNN models are significantly helpful in preventing landslides, and have higher prediction accuracy than other traditional machine learning and deep learning techniques.

NATURAL HAZARDS (2023)

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Article Engineering, Geological

On the effects of rheological behavior on landslide motion and tsunami hazard for the Baiyun Slide in the South China Sea

Zhiyuan Ren et al.

Summary: This study numerically investigates the effects of rheological behaviors on submarine landslide dynamics and tsunami hazard. The Herschel-Bulkley rheological theory and nonlinear shallow water equation model are utilized. The study reveals the considerable influence of material properties on landslide motion and associated tsunami amplitude, providing insights for landslide-generated tsunami assessment.

LANDSLIDES (2023)

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Article Environmental Sciences

Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China

Xiaoliang Dai et al.

Summary: Landslide susceptibility assessment in Liangshan, China was investigated using the geographical random forest (GRF) model. Compared to random forest (RF), GRF achieved higher performance with an AUC of 0.86 by considering spatial heterogeneity among variables. GRF also provided a higher-quality landslide susceptibility map, correctly identifying 92.35% of landslide points in high-susceptibility areas. The local feature importance derived from GRF revealed spatial variation in the impact of conditioning factors, providing implications for policy development to prevent and mitigate landslides.

REMOTE SENSING (2023)

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Article Green & Sustainable Science & Technology

Landslide Susceptibility Assessment Using the Analytic Hierarchy Process (AHP): A Case Study of a Construction Site for Photovoltaic Power Generation in Yunxian County, Southwest China

Jinxuan Zhou et al.

Summary: China is actively promoting clean energy construction to achieve carbon neutrality. However, engineering constructions in mountainous regions are vulnerable to landslide disasters. Therefore, it is crucial to assess the susceptibility of landslide disasters for disaster prevention and risk management in construction projects. This study conducted a field survey at 42 landslide points in the selected planned site region and assessed landslide susceptibility based on 11 impact factors using AHP method. The resulting landslide susceptibility zone map showed high accuracy, with an AUC value of 0.845 according to the ROC curve analysis.

SUSTAINABILITY (2023)

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Article Environmental Sciences

Slide type landslide susceptibility assessment of the Buyuk Menderes watershed using artificial neural network method

Senem Tekin et al.

Summary: This study assessed the landslide susceptibility in the Buyuk Menderes watershed using artificial neural network method and generated a susceptibility map. The map showed high accuracy and successfully predicted the locations of landslides in the study area.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

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Article Environmental Sciences

GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya

Suvam Das et al.

Summary: This study aimed to map the landslide susceptibility zones in and around the Kalimpong region by applying the Analytic Hierarchy Process (AHP) method integrated with fifteen causative factors. The best LSZ form for the study area was determined to be the SRC method, achieving a prediction accuracy of 79.5%. The results provide spatial information on areas that may face slope instability and can help government agencies and stakeholders in implementing appropriate measures.

ENVIRONMENTAL MONITORING AND ASSESSMENT (2022)

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Editorial Material Green & Sustainable Science & Technology

Renewable and sustainable energy challenges to face for the achievement of Sustainable Development Goals

Benedetto Nastasi et al.

Summary: This editorial introduces the virtual special issue of Renewable and Sustainable Energy Reviews (RSER) dedicated to the 4 SDEWES conferences held in 2020. It highlights that the issue includes high quality review papers and original research articles covering various areas such as renewable energy technologies, energy storage, energy modelling techniques, and climate and energy policy. The articles contribute to the Sustainable Development Goals (SDGs) and were selected after a rigorous peer review process.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

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Article Geosciences, Multidisciplinary

Monitoring of Maskun landslide and determining its quantitative relationship to different climatic conditions using D-InSAR and PSI techniques

Mohsen Pourkhosravani et al.

Summary: This study evaluates and monitors the displacement caused by landslides in Maskun, Iran using DInSAR and PSI techniques. It identifies the relationships between climatic conditions and mass displacement.

GEOMATICS NATURAL HAZARDS & RISK (2022)

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Article Geosciences, Multidisciplinary

Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions

Faming Huang et al.

Summary: In landslide susceptibility mapping (LSM), the expression of landslide boundaries and spatial shapes as points or circles instead of accurate polygons can lead to differences in the predicted landslide susceptibility indexes (LSIs) and introduce uncertainties into the LSM. This study compared the uncertainties of LSM modeling using different representations of landslide boundaries and spatial shapes, and found that using polygonal surfaces to represent the landslide boundaries can significantly improve the accuracy of LSM compared to using points and circles. The results also showed that polygon-based models have higher LSM accuracy compared to point- and circle-based models, and the overall accuracy of the random forest (RF) model is superior to that of the support vector machine (SVM) model.

GEOSCIENCE FRONTIERS (2022)

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Article Computer Science, Artificial Intelligence

A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Zewen Li et al.

Summary: This review provides insights into the development history of CNN, a overview of various convolutions, introduction to classic and advanced CNN models, conclusions drawn from experimental analysis, rules of thumb for function and hyperparameter selection, and applications of 1-D, 2-D, and multidimensional convolutions. Moreover, it also discusses open issues and promising directions for CNN as guidelines for future work.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Computer Science, Information Systems

Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation

Haowei Zeng et al.

Summary: This article proposes a graph neural network (GNN) constrained by environmental consistency (GNN-EC) for landslide susceptibility evaluation. The GNN-EC considers environmental heterogeneity and maintains high prediction accuracy, even with a small training set.

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2022)

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Article Chemistry, Multidisciplinary

Landslide Susceptibility Assessment by Using Convolutional Neural Network

Shahrzad Nikoobakht et al.

Summary: This study conducted a GIS-based landslide susceptibility assessment in northeastern Iran using a convolutional neural network (CNN), identifying geomorphologic/topographic and water condition parameters as the main triggering factors. The results showed that the CNN model outperformed other classifiers in terms of accuracy, precision, recall, and F1-score.

APPLIED SCIENCES-BASEL (2022)

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Article Chemistry, Multidisciplinary

CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data

Diego Renza et al.

Summary: In this study, a new convolutional neural network architecture is proposed for evaluating landslide susceptibility, trained with data obtained through geological, geomorphological, and land use information. The method shows high performance with low computational cost and pixel-level accuracy on test data.

APPLIED SCIENCES-BASEL (2022)

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Article Environmental Sciences

Runup of landslide-generated tsunamis controlled by paleogeography and sea-level change

Qiliang Sun et al.

Summary: Reconstruction of the paleo-shoreline of the South China Sea 0.54 million years ago reveals that the Baiyun landslide generated waves of around 18 meters in height, emphasizing the significance of shoreline reconstructions in studying prehistoric tsunamis.

COMMUNICATIONS EARTH & ENVIRONMENT (2022)

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Article Environmental Studies

Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Siti Norsakinah Selamat et al.

Summary: The research successfully produced a landslide susceptibility map for the Langat River Basin in Malaysia using an Artificial Neural Network (ANN). The study found rainfall to be the most crucial factor affecting landslide occurrence in the area.

LAND (2022)

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Article Geosciences, Multidisciplinary

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

Phuong Thao Thi Ngo et al.

Summary: In this study, a national-scale landslide susceptibility mapping of Iran was conducted using recurrent neural network (RNN) and convolutional neural network (CNN) algorithms. RNN algorithm outperformed CNN algorithm in both training and testing phases, with 6% and 14% of Iran's land area being highly susceptible to future landslide events. Additionally, 31% of cities in Iran are located in high and very high landslide susceptibility areas.

GEOSCIENCE FRONTIERS (2021)

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Review Geosciences, Multidisciplinary

Pathways and challenges of the application of artificial intelligence to geohazards modelling

Abhirup Dikshit et al.

Summary: The application of artificial intelligence and machine learning in geohazard modeling has been rapidly growing, bringing new possibilities for practical applications in geohazards through data-driven research. Future developments should focus on consecutive events, integrating physical models, and developing interpretable models capable of handling large amounts of data.

GONDWANA RESEARCH (2021)

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Article Geosciences, Multidisciplinary

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

Husam A. H. Al-Najjar et al.

Summary: This research presents a novel method using Generative Adversarial Networks (GANs) to create synthetic inventory data for landslide prediction improvement, specifically in Cameron Highlands, Malaysia. The study demonstrates that utilizing GANs to generate supplementary samples can enhance the predictive capability of common landslide prediction models in data-scarce environments.

GEOSCIENCE FRONTIERS (2021)

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A survey on anomaly detection for technical systems using LSTM networks

Benjamin Lindemann et al.

Summary: This article surveys state-of-the-art anomaly detection techniques, focusing on using deep neural networks and especially long short-term memory networks for anomaly detection. The approaches are evaluated based on application scenarios, data, anomaly types, and other metrics, highlighting the potential of upcoming anomaly detection techniques, including graph-based and transfer learning approaches.

COMPUTERS IN INDUSTRY (2021)

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Assessment of shallow landslide susceptibility using an artificial neural network

Bin Zeng et al.

Arabian Journal of Geosciences (2021)

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Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks

Luisa Vieira Lucchese et al.

Summary: Research indicates that intervening in nonoccurrence samples may result in models unsuitable for generalization in using Artificial Neural Networks to assess landslide susceptibility. Careful handling of biased samples is crucial to avoid artificially inflated metrics in landslide susceptibility assessments using ANN.

CATENA (2021)

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Review Engineering, Electrical & Electronic

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Wojciech Samek et al.

Summary: With the increasing demand for explainable artificial intelligence (XAI) due to the successful usage of machine learning, particularly deep neural networks, this work aims to provide an overview of the field, test interpretability algorithms, and demonstrate successful usage in application scenarios.

PROCEEDINGS OF THE IEEE (2021)

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Deep learning-based landslide susceptibility mapping

Mohammad Azarafza et al.

Summary: The study developed a CNN-DNN model for landslide susceptibility mapping in Isfahan province, demonstrating high prediction accuracy compared to benchmark machine learning techniques. The model revealed high-susceptibility areas in the west and southwest of Isfahan province, proving useful for landslide risk management and land use planning.

SCIENTIFIC REPORTS (2021)

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Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks

Rui-Xuan Tang et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2020)

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Artificial neural network ensembles applied to the mapping of landslide susceptibility

L. Bragagnolo et al.

CATENA (2020)

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Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas

Juan Du et al.

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Typical displacement behaviours of slope movements

M. R. Scoppettuolo et al.

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Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

Zhice Fang et al.

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On the landslide tsunami uncertainty and hazard

Finn Lovholt et al.

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Probabilistic mapping of earthquake-induced submarine landslide susceptibility in the South-West Iberian margin

Stefano Collico et al.

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Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region

Yaning Yi et al.

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Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

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Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

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GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea)

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