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Modeling the Spatial Distribution of Debris Flows and Analysis of the Controlling Factors: A Machine Learning Approach

Yan Zhao, Xingmin Meng, Tianjun Qi, Guan Chen, Yajun Li, Dongxia Yue, Feng Qing
2021 Remote Sensing  
For analysis, 52 influencing factors, statistical, machine learning, remote sensing and GIS methods were used to analyze the spatial distribution and controlling factors of 652 debris flow catchments with  ...  Debris flows are a major geological hazard in mountainous regions. For improving mitigation, it is important to study the spatial distribution and factors controlling debris flows.  ...  Acknowledgments: The DEM data were provided by the International Scientific and Technical Data Mirror Site, Computer Network Information Center, Chinese Academy of Sciences.  ... 
doi:10.3390/rs13234813 fatcat:5vqijwikkjhstiodxpx4r3nefm

Landslide Susceptibility Assessment of Wildfire Burnt Areas through Earth-Observation Techniques and a Machine Learning-Based Approach

Mariano Di Napoli, Palmira Marsiglia, Diego Di Martire, Massimo Ramondini, Silvia Liberata Ullo, Domenico Calcaterra
2020 Remote Sensing  
burnt areas through band compositions of satellite images; and (2) landslide susceptibility assessment through the application of a new statistical approach (machine learning techniques).  ...  debris flow).  ...  Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2020, 12, 2505  ... 
doi:10.3390/rs12152505 fatcat:2qfvgoyf7fhsvikxxn34pbyaci

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

Yves Rybarczyk, Rasa Zalakeviciute
2018 Applied Sciences  
To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques.  ...  The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning  ...  These results are mainly based on descriptive statistics regarding the number of studies that uses a machine learning approach over the last decade, the geographic distribution of these studies across  ... 
doi:10.3390/app8122570 fatcat:6syilibpvjgjxh75eiobjynl34

A machine learning approach for mapping the very shallow theoretical geothermal potential

Dan Assouline, Nahid Mohajeri, Agust Gudmundsson, Jean-Louis Scartezzini
2019 Geothermal Energy  
Geographic Information Systems, traditional modelling, and machine learning (ML).  ...  This effort is motivated by the Swiss Energy Strategy 2050, which sets as a goal to cease the use of nuclear power as a part of the energy mix by 2035, and reduce the CO 2 emissions by factor of 70 % by  ...  Acknowledgements We thank the reviewers for very thorough reviews and helpful comments on an earlier version of this paper that significantly improved its final version.  ... 
doi:10.1186/s40517-019-0135-6 fatcat:26dvl7ckivdlpc5uq3wgzgx3h4

Susceptibility Assessment of Debris Flows Coupled with Ecohydrological Activation in the Eastern Qinghai-Tibet Plateau

Hu Jiang, Qiang Zou, Bin Zhou, Zhenru Hu, Cong Li, Shunyu Yao, Hongkun Yao
2022 Remote Sensing  
There is thus an urgent need in this region to conduct a regional-scale debris flow susceptibility assessment to determine the spatial likelihood of a debris flow occurrence and guarantee the safety of  ...  relief and active tectonics) that control the occurrence of debris flows, which are rapid, surging flows of water-charged clastic sediments moving along a steep channel and are one of the most dangerous  ...  Model Training and Evaluation The relationship between disaster-causing factors and debris flow occurrence can be quantified by model training with a set of weights and bias parameters of machine learning  ... 
doi:10.3390/rs14061444 fatcat:yhhcgisnv5a3disis57x27e5a4

Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China

Ke Xiong, Basanta Raj Adhikari, Constantine A. Stamatopoulos, Yu Zhan, Shaolin Wu, Zhongtao Dong, Baofeng Di
2020 Remote Sensing  
Four models were constructed based on the debris flow inventory and a range of causal factors.  ...  As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12020295 fatcat:kflwq77bvbcsxocqgevv7bib4y

Non-recurrent Traffic Congestion Detection with a Coupled Scalable Bayesian Robust Tensor Factorization Model [article]

Qin Li, Huachun Tan, Xizhu Jiang, Yuankai Wu, Linhui Ye
2020 arXiv   pre-print
And, it naturally captures the high-dimensional spatial-temporal structural properties of traffic data by tensor factorization.  ...  The framework can couple multivariable traffic data including traffic flow, road speed, and occupancy through sharing a similar or the same sparse structure.  ...  And the authors wish to express their appreciation and gratitude to their colleague, Yong Li (an associate professor working with Beijing University of Posts and Telecommunications, China), for reading  ... 
arXiv:2005.04567v1 fatcat:vynenzvho5c7dlsq4lrqbw656e

Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)

Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, Mario Floris, Filippo Catani
2022 Natural Hazards and Earth System Sciences  
This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province  ...  The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models.  ...  machine learning models of RF and XGBoost.  ... 
doi:10.5194/nhess-22-1395-2022 fatcat:c7rjyjr57vdi5adfvvpj77gbgm

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

Xiangang Luo, Feikai Lin, Shuang Zhu, Mengliang Yu, Zhuo Zhang, Lingsheng Meng, Jing Peng, Claudionor Ribeiro da Silva
2019 PLoS ONE  
Predictive capability of these factors was evaluated by using the value of variance inflation factor and information gain ratio.  ...  Three models, namely artificial neural network (ANN), support vector machine (SVM) and information value model (IVM), were applied to assess the mine landslide sensitivity.  ...  [4] used the information value model with seven environmental factors to evaluate debris flow susceptibility. Chen et al.  ... 
doi:10.1371/journal.pone.0215134 pmid:30973936 pmcid:PMC6459520 fatcat:2lzmtdtzhra5pcpmx23sen6tpu

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

Yonghong Zhang, Taotao Ge, Wei Tian, Yuei-An Liou
2019 Remote Sensing  
The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows.  ...  Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks.  ...  Acknowledgments: The authors would like to thank the Tibet Plateau Institute of Atmospheric Environment, Geospatial data cloud, Resource and Environmental Cloud Platform, earth observing system data and  ... 
doi:10.3390/rs11232801 fatcat:eqkhwch35bg5fm3jn3pzczhbv4

Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

Prima Kadavi, Chang-Wook Lee, Saro Lee
2018 Remote Sensing  
The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models  ...  A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10081252 fatcat:cyukg65d25dqxinso3sj4323ke

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Xin Yang, Rui Liu, Mei Yang, Jingjue Chen, Tianqiang Liu, Yuantao Yang, Wei Chen, Yuting Wang
2021 Remote Sensing  
To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models.  ...  The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method.  ...  In addition, Xin Yang wants to thank, in particular, the care and support from Mengtian Li during the COVID-19 epidemic. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13112166 fatcat:vxhukghotzhpnctlw2v3vfvuxm

A Spatial Ensemble Model for Rockfall Source Identification from High Resolution LiDAR Data and GIS

Ali Mutar Fanos, Biswajeet Pradhan
2019 IEEE Access  
This difficulty rises in the areas where there is a presence of other types of the landslide, such as shallow landslide and debris flow.  ...  The aim of this paper is to develop and test a hybrid model that can accurately identify the source areas.  ...  ACKNOWLEDGMENT The authors wish to thank the Department of Mineral and Geosciences, the Department of Surveying Malaysia, the Federal Department of Town and Country Planning Malaysia for the data provided  ... 
doi:10.1109/access.2019.2919977 fatcat:3a2jkb7bkzaqtba5xegnp7u4oa

Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal

Pawan Gautam, Tetsuya Kubota, Aril Aditian
2021 Geoenvironmental Disasters  
We assessed the landslides distribution in terms of density, number, and area within 85 classes of 13 causal factors including slope, aspect, elevation, formation, land cover, distance to road and river  ...  The LSM approach showed good accuracy with respective AUC values for success rate and prediction rate of 0.843 and 0.832.  ...  The authors also would like to thank anonymous reviewers for the constructive comments for the improvement of this manuscript. The authors were indebted to Roma for English checks.  ... 
doi:10.1186/s40677-021-00200-3 fatcat:olyg5xzw2nbi5d2c2btdav4pci

Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling

Jason Goetz, Robin Kohrs, Eric Parra Hormazábal, Manuel Bustos Morales, María Belén Araneda Riquelme, Cristián Henríquez, Alexander Brenning
2021 Natural Hazards and Earth System Sciences  
Knowing the source and runout of debris flows can help in planning strategies aimed at mitigating these hazards.  ...  Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo River basin in the Andes of Santiago  ...  This approach should provide a rigorous estimate of the spatial transferability of a model by attempting to reduce spatial autocorrelation between test and training data (Brenning, 2005; Wenger and Olden  ... 
doi:10.5194/nhess-21-2543-2021 fatcat:3lyqkx3dj5covekn5l22rrejje
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