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Article
Engineering, Geological
Chenyang Zhang et al.
Summary: This study investigates the deformation and failure characteristics of multi-row stabilizing piles reinforced reservoir landslides through two centrifuge tests. The results show that the lowering of the reservoir water level induces increased bending moments at the lower section of the piles, leading to bending deformation and failure near the sliding zone. A smaller row spacing enhances the mechanical connection between the piles and improves the overall reinforcement capacity, while a larger row spacing weakens the connection and results in independent mechanical states for each row of piles.
Article
Engineering, Environmental
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)
Article
Computer Science, Interdisciplinary Applications
Ilyas Ahmad Huqqani et al.
Summary: This study explores the application of two metaheuristic algorithms (ABC and AFS) in landslide susceptibility mapping. By integrating these two algorithms with ANN model, the optimal computational parameters can be found. The results show that the predictive ability of the ABC-ANN model is better in optimizing the computational parameters and structure compared to the AFS-ANN model.
ENGINEERING WITH COMPUTERS
(2023)
Article
Geosciences, Multidisciplinary
Deliang Sun et al.
Summary: This study compared assessment models of landslide susceptibility along mountain highways using different machine learning algorithms and mapping units. The results showed that after removing noise-generating factors, the model trained with the remaining landslide-conditioning factors achieved ideal forecast accuracy. The slope unit provided more accurate and reasonable evaluation results, and the RF model outperformed the SVM model.
Article
Geosciences, Multidisciplinary
Shuai Chen et al.
Summary: In this study, an empirical model was developed to quantify the spatial heterogeneity of rock mass strength based on an analysis of a seismic landslide inventory and lithological environment. The results showed that considering the rock mass strength heterogeneity significantly improved the accuracy of seismic landslide susceptibility assessment. These findings are valuable for earthquake emergency rescue and post-earthquake land planning.
GEOMATICS NATURAL HAZARDS & RISK
(2023)
Article
Environmental Sciences
Shuyue Ma et al.
Summary: The deformation characteristics and instability patterns of rotational landslides were analyzed using techniques such as InSAR, DODs, and numerical simulations. The topographic changes, surface deformation, and movement process before, during, and after a landslide were examined. The active zone of the landslide was identified based on high-resolution terrain data. The displacement characteristics and kinematic behavior were summarized and compared with those of a single rotational landslide and multiple rotational landslides.
Article
Engineering, Multidisciplinary
Rana Muhammad Adnan et al.
Summary: This study evaluates the prediction accuracy of two newly developed heuristic methods (OPELM and DENFIS) in drought modeling and compares them with multivariate adaptive regression spline (MARS). The results show that the DENFIS model performs better than the OP-ELM and MARS models for SPI3, SPI6, and SPI12 drought indexes, except for SPI6 where the OP-ELM model performs better. It is also found that adding periodicity as inputs generally improves forecasting results, but in some cases, it negatively affects the accuracy of the models. The best DENFIS model improves the accuracy of the OP-ELM model by 8.35%, 6.06%, and 2.51% for predicting SPI3, SPI6, and SPI12, respectively.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Geosciences, Multidisciplinary
Wubiao Huang et al.
Summary: A new model called Conv-SE-LSTM is proposed for landslide susceptibility mapping along the Sichuan-Tibet transportation corridor. This model adaptively emphasizes the contribution of conditioning factors and utilizes their dependence by integrating Squeeze and Excitation network (SE) and long short-term memory network (LSTM). The proposed model demonstrates better performance compared to traditional methods, with a higher Area Under Curve (AUC) value of 0.8813. Additionally, an annual scale landslide susceptibility changes analysis method is presented with a high accuracy rate of 93.33%, revealing the dynamic response relationship between landslide susceptibility and conditioning factors.
Article
Biodiversity Conservation
Haoyuan Hong
Summary: Landslide susceptibility mapping is important for reducing the impact of landslides. This study proposes a hybrid model that combines the Best-first decision tree (BFT) model with other models to assess their performance. A landslide inventory map was created using 364 landslides and non-landslide data in Yongxin County, China. The Support vector machines (SVM) method was used to determine the most useful factors for modeling. The results demonstrate that the hybrid models outperform the single BFT model, with BFT-D and BFT-B being the most effective models for landslide susceptibility modeling. These models can aid in land use planning and infrastructure development in Yongxin County.
ECOLOGICAL INDICATORS
(2023)
Article
Environmental Sciences
Junyi Zhang et al.
Summary: The study aimed to construct an explanatory framework for landslide susceptibility evaluation models based on the SHAP-XGBoost algorithm. By selecting typical mountainous regions, a geospatial database containing 12 influencing factors was constructed, and the prediction results of the model were explained using the SHAP algorithm. The study found that the spatial distribution of landslides is heterogeneous and complex, and the contribution of each influencing factor has regional characteristics and spatial heterogeneity. The generalizability of the landslide susceptibility evaluation model is spatially heterogeneous, with better generalizability to regions with similar characteristics.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Engineering, Geological
Yanqian Pei et al.
Summary: This study reveals that climate change affects the occurrence of landslides in the land cryosphere, posing a severe threat to downstream communities. The research also shows that there is an elevation dependence of landslide activity, particularly at higher altitudes where increased temperatures lead to more landslides.
Review
Fisheries
Zhixin Liu et al.
Summary: Currently, many cities are dealing with serious water resource issues caused by urbanization. Remote sensing and geostatistics have been widely used in urban water resource monitoring with the advancements in technology. The aim of this study is to review the application of remote sensing and geostatistics in monitoring urban water resources and discuss their further development. The research methods involve bibliometric analysis of existing literature in this field, and exploration of the use of remote sensing and geostatistics in improving urban water resource monitoring capacity. The study concludes that remote sensing and geostatistics can greatly enhance the monitoring capacity of urban water resources, leading to more efficient utilization.
MARINE AND FRESHWATER RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Rui Liu et al.
Summary: This study explores landslide susceptibility mapping (LSM) based on different evaluation units and proposes a strategy for landslides' differential characteristics in different sub-regions. The LGBM-TUs model showed the highest performance and lithology, elevation, and average annual rainfall were the dominant factors. The results provide novel insights into landslide mitigation and propose a new method for understanding the spatial differential characteristics of landslides in various sub-regions.
GEOMATICS NATURAL HAZARDS & RISK
(2023)
Article
Environmental Sciences
Cheng Gao et al.
Summary: Based on the analysis of flood disasters and their influencing factors, an index system was constructed for the Wuchengxiyu Region using physical geography and social economy data. The system included factors that induce disasters, the environment conducive to disasters, and the vulnerability of the affected areas. Weightings for each index were determined using the analytic hierarchy process, and a comprehensive evaluation index was established using weighted methods. By utilizing Geographic Information System spatial analysis techniques, a flood risk zoning model was developed for the Wuchengxiyu Region. The results identified high-risk areas in districts of Wuxi City, Changzhou City, and Jiangyin City, with different factors contributing to their vulnerability.
Article
Computer Science, Interdisciplinary Applications
Michele Placido Antonio Gatto et al.
Summary: This paper presents a multi-approach algorithm, based on a simplified physically-based model, for predicting soil slips over large areas. The algorithm overcomes the limitations of AI-driven methods, which are not suitable for temporal prediction. Using different approaches, the algorithm evaluates model parameters for grid pixels based on easily available territorial data. The algorithm is validated in the Emilia-Romagna Region of Italy and demonstrates excellent prediction capabilities with an AUC > 80%.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Water Resources
Rana Muhammad Adnan et al.
Summary: Accurate measurement of water resources is crucial for achieving a sustainable environment. This study presents the development and verification of hybrid extreme learning machine (ELM) models coupled with metaheuristic methods for monthly streamflow prediction. The results showed that the ELM-SAMOA and ELM-PSOGWO models offered the best accuracy compared to other models. These models can be successfully applied in modeling monthly streamflow prediction with local or external hydro-meteorological datasets.
APPLIED WATER SCIENCE
(2023)
Article
Environmental Sciences
Saeid Shabani et al.
Summary: This study modeled and spatially visualized the susceptibility of a forest stand in northern Iran to snowstorm damage using the random forest (RF) and logistic regression (LR) methods. The RF model outperformed the LR model in both training and validation phases, identifying slope, aspect, and wind effect as the variables with the greatest impacts on forest stand sustainability to snowstorm damage. Approximately 30% of the study area was categorized as highly and very highly susceptible to snowstorms. The results can inform forest managers in developing adaptive forest management plans for snowstorm readiness and recovery.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Haijia Wen et al.
Summary: This study proposes a quantitative assessment model for landslide-induced long-distance pipeline risk by analyzing historical landslide hazard data. The approach combines recursive feature elimination and particle swarm optimization-AdaBoost method for landslide susceptibility mapping, as well as fuzzy clustering and CRITIC method for pipeline vulnerability assessment. The results show high susceptibility and vulnerability in certain areas, and the proposed hybrid machine learning model can provide a scientific risk classification for pipeline planning and operation in mountainous areas.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Geosciences, Multidisciplinary
Jie Chen et al.
Summary: Seismic activity is complex and random, and its temporal and spatial distribution exhibits complexity, stages, levels, and inheritance. Studying the temporal and spatial distribution characteristics of seismic activity is of great significance for understanding the law of seismic activity, predicting seismic risk, and other earthquake-related research. In this study, the seismic data catalog of the Eurasian seismic belt was selected as the research object, and the multifractal analysis method was used to analyze the seismic data based on the characteristics of the seismic geological and tectonic environment. The results showed that the seismic activity of seismic zones exhibits a clear multifractal structure in time series and spatial scales, effectively revealing the seismic characteristics of seismic activity in time and space.
Article
Environmental Sciences
Ning Yang et al.
Summary: In this paper, a semi-integrated supervised approach was proposed to improve the prediction performance of machine learning models in landslide susceptibility studies. The approach combined different models to form an integrated weighted model and utilized remote sensing images for sample identification and improvement of accuracy. The results showed that the proposed method successfully solved the common problems in landslide susceptibility studies and improved the performance of the traditional machine learning models.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Jean-Claude Maki Mateso et al.
Summary: In the rift flank west of Lake Kivu in DR Congo, forest cover dynamics, roads, and mining activities have significant impacts on landslide characteristics and causes. Deforestation leads to more frequent but smaller-sized shallow landslides due to the reduction in regolith cohesion. Mining activities increase the odds of landslides, and landslides associated with roads are larger than shallow landslides but smaller than recent deep-seated instabilities.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2023)
Article
Environmental Sciences
Luca Mauri et al.
Summary: This study aims to model multi-temporal overland flow dynamics in a shallow landslides-prone terraced landscape in northern Italy by using Remotely Piloted Aircraft Systems (RPAS) and photo-grammetric techniques. The study analyzes the impact of roads on water flow depth alterations through hydrological analyses. Results show that roads change water flow paths and suggest measures to reduce similar future scenarios.
INTERNATIONAL SOIL AND WATER CONSERVATION RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Qihang Li et al.
Summary: This study simulated the deformation process of a large and steep rock slope in China under variable rainfall, finding that the increased deformation region is positively correlated with increasing pore water pressure and water content values, while the infiltration of rainfall softens weak interlayers and leads to failure of the slope toe first, followed by the middle and upper parts sliding and failing sequentially. The proposed improved region growing segmentation method showed a significantly reduced average identification error in X and Y directions compared to the original method, indicating its potential for high-precision identification of rock slope deformation in complex scenes.
Editorial Material
Multidisciplinary Sciences
Jing Zhou et al.
Article
Environmental Sciences
Saeed Alqadhi et al.
Summary: The study utilized various machine learning methods to construct landslide susceptibility maps and conducted sensitivity analysis to determine the most relevant conditioning factors, ultimately establishing a highly robust LSM based on important parameters. The final model showed promising accuracy and highlighted the importance of selecting conditioning parameters carefully to ensure the resilience and precision of LSM.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Wencheng Wang et al.
Summary: This paper proposes an adaptive and simple color image enhancement method based on the improved logarithmic transformation, which applies the Weber-Fechner law to grayscale mapping in logarithmic space. By adaptively adjusting the parameters of the illumination distribution, the method improves the brightness and color saturation of the image while reducing the impact of non-uniform illumination. Experimental results show improved performance compared to existing image enhancement methods.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2022)
Article
Multidisciplinary Sciences
Tomas Panek et al.
Summary: The distribution of landslides in deglaciated mountains can be opposite to traditional assumptions, with larger landslides concentrated in less tectonically active and drier areas.
SCIENTIFIC REPORTS
(2022)
Article
Environmental Sciences
Guoqing Zhou et al.
Summary: This paper proposes a multiattention generative adversarial network model to fill the voids in digital elevation models (DEM) acquired through photogrammetry or LiDAR. Experimental results show that the proposed model achieves high structural similarity in different types of terrains, improving the accuracy of DEM.
Article
Forestry
Yakun Zhang et al.
Summary: Landslides are common natural disturbances in forest ecosystems, which alter soil properties such as chemical and microbial characteristics. This study investigated the influence of landslides on soil properties in a temperate secondary forest in China and found that landslides significantly reduced soil carbon, nitrogen, and phosphorus contents, as well as nitrate concentration and microbial activities. Restoration of original soil properties after landslides may require a long time.
JOURNAL OF FORESTRY RESEARCH
(2022)
Article
Geography, Physical
Ghislain Zangmo Tefogoum et al.
Summary: This study aims to study and map the factors of mass movement hazards in Djounde ' and its surroundings in Maroua, Cameroon, and produce a hazard map. The most frequent mass movements in the study area are falls of rock blocks and debris, as well as debris flows. These phenomena are induced by natural and anthropogenic factors. Predisposing factors include cracks and fissures on rocks, steep slopes, vegetation cover, rainfall, and temperature. Triggering factors include the opening of quarries and summer rainfall. Approximately 35%, 40%, and 25% of the study area have high, medium, and low probabilities of mass movement initiation, respectively.
Article
Geochemistry & Geophysics
C. D. Trong et al.
Summary: This paper presents an analysis of geomorphological indices for predicting the maximum observed earthquake in a research region. The study finds that certain geomorphological indices are correlated with earthquake magnitude, providing insights into earthquake prediction.
Review
Environmental Sciences
Junpeng Huang et al.
Summary: This study assesses the hotspots and research trends on Geographic Information System (GIS)-based Landslide Susceptibility (LS) using a combination of bibliometric and content analysis. The findings reveal clear clusters of high academic activity among authors, institutions, and countries, and highlight key problems in landslide conditioning factors and the development trends of LS models.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Computer Science, Information Systems
Weilian Li et al.
Summary: Virtual scenes can enhance public disaster perception, but current methods for constructing debris flow disaster scenes have deficiencies in considering public knowledge and enhancing semantic information while emphasizing visual effects.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2021)
Article
Engineering, Aerospace
Sunil Saha et al.
Summary: The study aimed to generate a landslide susceptibility map for the Sikkim Himalayan region using machine learning approaches, and found that the RS-RF model with a 70:30 sample ratio showed the highest goodness-of-fit and accuracy.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Engineering, Environmental
Seyed Vahid Razavi-Termeh et al.
Summary: This research utilized the ANFIS algorithm in conjunction with the ACOR and DE algorithms to provide a landslide susceptibility map of the Fahliyan sub-basin. The results indicated that the distance to road, rainfall, and SPI were the most significant factors influencing landslide occurrence in the area.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2021)
Article
Geosciences, Multidisciplinary
Xiaoliang Xie et al.
Summary: This paper aims to better predict future cyclone impacts using big data analysis techniques, utilizing large cyclone data sets from the CMA Tropical Cyclone Data Center and using methods such as Hausdorff distance and Monte Carlo techniques to estimate impact probabilities.
Article
Green & Sustainable Science & Technology
Adeli Beatriz Braun et al.
Summary: This study developed a comprehensive list of 63 sustainable remediation indicators, validated them with experts from around the world, and determined their weights through a Likert scoring scale. Experts showed a greater emphasis on environmental and social indicators over economic ones, indicating a balance in sustainability dimensions.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Environmental Sciences
Abu Reza Md Towfiqul Islam et al.
Summary: This study aims to predict and map nitrate concentration susceptibility in the coastal multi-aquifers of Bangladesh using a novel framework approach. By employing ensemble learning algorithms and cross-validation methods, the study identified depth, pH, and As as the most influential factors affecting nitrate concentration in groundwater. The Boosting model outperformed other models in mapping nitrate concentration susceptibility, showcasing the effectiveness of the proposed approach for groundwater pollution management.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Engineering, Geological
Shuai Huang et al.
Summary: The study found that soil slopes in regions with higher rainfall have different seismic performances at various groundwater levels, with significantly increased deformation at high groundwater levels but reduced vibration due to the presence of groundwater. In addition, the influence of groundwater leads to different failure processes in the slope.
Article
Computer Science, Information Systems
Guoqing Zhou et al.
Summary: This study introduces a generalized buffering algorithm (GBA) to meet the high accuracy demands of buffer analysis. Experimental results indicate that the proposed GBA can improve the deficiencies and accuracy of traditional buffering algorithms (TBA) in practical applications.
Article
Geosciences, Multidisciplinary
Yanli Wu et al.
Article
Chemistry, Analytical
Mohammad Mehrabi et al.
Review
Geosciences, Multidisciplinary
Abdelaziz Merghadi et al.
EARTH-SCIENCE REVIEWS
(2020)
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Environmental Sciences
Mousa Abedini et al.
GEOCARTO INTERNATIONAL
(2019)
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Forestry
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Environmental Sciences
Haifeng Tian et al.
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Engineering, Geological
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ENGINEERING GEOLOGY
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Article
Geosciences, Multidisciplinary
Hossein Moayedia et al.
GEOMATICS NATURAL HAZARDS & RISK
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Article
Geosciences, Multidisciplinary
Haoyuan Hong et al.
Article
Environmental Sciences
Binh Thai Pham et al.
INTERNATIONAL JOURNAL OF SEDIMENT RESEARCH
(2018)
Article
Computer Science, Interdisciplinary Applications
Harley Betts et al.
ENVIRONMENTAL MODELLING & SOFTWARE
(2017)
Article
Geosciences, Multidisciplinary
Wei Chen et al.
Article
Geography, Physical
Marko Komac et al.
Article
Geography, Physical
Kang-tsung Chang et al.
Article
Geosciences, Multidisciplinary
Fausto Guzzetti et al.
EARTH-SCIENCE REVIEWS
(2012)
Review
Computer Science, Artificial Intelligence
Lior Rokach
ARTIFICIAL INTELLIGENCE REVIEW
(2010)
Article
Engineering, Geological
FC Dai et al.
ENGINEERING GEOLOGY
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