Method for monitoring and forecasting landslide phenomenon based on machine learning

被引:1
作者
Nguyen, Van-Tinh [1 ]
Nguyen, Quang-Anh [1 ]
Nguyen, Ngoc-Kien [1 ]
机构
[1] Hanoi Univ Sci & Technol, Sch Mech Engn, Hanoi, Vietnam
关键词
Landslide; Machine learning; Forecasting; Monitoring; LOCATING LANDSLIDES;
D O I
10.1016/j.mex.2024.102797
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A landslide involves the downward movement of a mass of rock, debris, earth, or soil. Landslides happen when gravitational forces and other types of shear stresses on a slope surpass the shear strength of the materials. Additionally, landslides can be triggered by processes that weaken the shear strength of the slope's material. Shear strength primarily depends on two factors such as frictional strength, which is the resistance to movement between the interacting particles of the slope material, and cohesive strength, which is the bonding between those particles. A landslide is a terrible natural disaster that causes much damage to both human life and the economy. It often occurs in steep mountainous areas or hilly regions, ranging in scale from medium to large. It progresses slowly (20-50 mm/year), but when it occurs, it can move at a speed of 3 m/s. Therefore, early detection or prevention of this disaster is an essential and significant task. This paper developed a method to collect and analyze data, with the purpose of determining the possibility of landslide occurrences to reduce its potential losses. center dot The proposed method is convenient for users to grasp information of landslide phenomenon. center dot A machine learning model is applied to forecast landslide phenomenon. center dot Internet of things (IoT) system is utilized to manage and send a warning text to individual email address and mobile devices.
引用
收藏
页数:9
相关论文
共 50 条
[41]   Forecasting realized volatility of crude oil futures prices based on machine learning [J].
Luo, Jiawen ;
Klein, Tony ;
Walther, Thomas ;
Ji, Qiang .
JOURNAL OF FORECASTING, 2024, 43 (05) :1422-1446
[42]   Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems [J].
Tercha, Wassila ;
Tadjer, Sid Ahmed ;
Chekired, Fathia ;
Canale, Laurent .
ENERGIES, 2024, 17 (05)
[43]   Study of Forecasting and Estimation Methodology of Oilfield Development Cost Based on Machine Learning [J].
Shi Mingyu ;
Wang Jianjun ;
Yi Chenggao ;
Chen Rong ;
He Yuanyuan .
Chemistry and Technology of Fuels and Oils, 2021, 56 :1000-1019
[44]   Study of Forecasting and Estimation Methodology of Oilfield Development Cost Based on Machine Learning [J].
Shi Mingyu ;
Wang Jianjun ;
Yi Chenggao ;
Chen Rong ;
He Yuanyuan .
CHEMISTRY AND TECHNOLOGY OF FUELS AND OILS, 2021, 56 (06) :1000-1019
[45]   A Machine Learning-Based Model for Epidemic Forecasting and Faster Drug Discovery [J].
Stergiou, Konstantinos D. ;
Minopoulos, Georgios M. ;
Memos, Vasileios A. ;
Stergiou, Christos L. ;
Koidou, Maria P. ;
Psannis, Konstantinos E. .
APPLIED SCIENCES-BASEL, 2022, 12 (21)
[46]   Hierarchical Extreme Learning Machine Enabled Dynamic Line Rating Forecasting [J].
Saatloo, Amin Mansour ;
Moradzadeh, Arash ;
Moayyed, Hamed ;
Mohammadpourfard, Mostafa ;
Mohammadi-Ivatloo, Behnam .
IEEE SYSTEMS JOURNAL, 2022, 16 (03) :4664-4674
[47]   Flood Forecasting Using Machine Learning: A Review [J].
Ghorpade, Parag ;
Gadge, Aditya ;
Lende, Akash ;
Chordiya, Hitesh ;
Gosavi, Gita ;
Mishra, Asima ;
Hooli, Basavaraj ;
Ingle, Yashwant S. ;
Shaikh, Nuzhat .
2021 8TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS (ICSCC), 2021, :32-36
[48]   Machine Learning Advances for Time Series Forecasting [J].
Masini, Ricardo P. ;
Medeiros, Marcelo C. ;
Mendes, Eduardo F. .
JOURNAL OF ECONOMIC SURVEYS, 2023, 37 (01) :76-111
[49]   Forecasting Corn Yield With Machine Learning Ensembles [J].
Shahhosseini, Mohsen ;
Hu, Guiping ;
Archontoulis, Sotirios V. .
FRONTIERS IN PLANT SCIENCE, 2020, 11
[50]   Forecasting unemployment in the euro area with machine learning [J].
Gogas, Periklis ;
Papadimitriou, Theophilos ;
Sofianos, Emmanouil .
JOURNAL OF FORECASTING, 2022, 41 (03) :551-566