Land subsidence susceptibility assessment using random forest machine learning algorithm

The mechanism of land subsidence and soil deformation deals with the dissipation of excess pore water pressure and the compaction of soil skeleton under the effect of natural or man-made factors, which can lead to serious disasters in the process of urbanization. The negative effects of land subside...

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Bibliographic Details
Published in:Environmental earth sciences Vol. 78; no. 16; pp. 1 - 12
Main Authors: Mohammady, Majid, Pourghasemi, Hamid Reza, Amiri, Mojtaba
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019
Springer Nature B.V
Subjects:
Algorithms
Arid regions
Artificial intelligence
Biogeosciences
buildings
Deformation
Disaster management
Disasters
Earth
Earth and Environmental Science
Earth Sciences
Environmental protection
Environmental Science and Engineering
forestry equipment
Geochemistry
geographic information systems
Geographical information systems
Geology
graphs
groundwater
Groundwater table
Hydrology/Water Resources
Hydrostatic pressure
Industrial areas
infrastructure
Iran
Land subsidence
Land use
Learning algorithms
Learning theory
Machine learning
Mapping
Morphology
Original Article
pipelines
Plains
Pore pressure
Pore water
Pore water pressure
Precipitation rate
prioritization
Soil
Soil compaction
Soils
Structural damage
Submarine pipelines
Subsidence
Sustainable development
Terrestrial Pollution
Urbanization
Water pressure
Water table
Iran
Land subsidence
Random forest
Groundwater
Iran
Mean decrease Gini
ISSN:1866-6280, 1866-6299
Online Access:Get full text
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Summary:The mechanism of land subsidence and soil deformation deals with the dissipation of excess pore water pressure and the compaction of soil skeleton under the effect of natural or man-made factors, which can lead to serious disasters in the process of urbanization. The negative effects of land subsidence include structural and fundamental damages to underground and aboveground infrastructures such as pipelines and buildings, changes in land surface morphology, and creation of earth fissures. Arid and semi-arid countries like Iran are highly prone to land subsidence phenomenon. In these regions, precipitation rate and natural recharges are relatively lower than those of the global average showing the importance of ground waters for agricultural and industrial activities. Land subsidence has already occurred in more than 300 plains in Iran. Semnan Plain is one of the most important areas facing this phenomenon. The purpose of this research was to assess land subsidence susceptibility using random forest machine learning theory. At first, prioritization of conditioning factors was done using random forest method. Results showed that distance from fault, elevation, slope angle, land use, and water table have the greatest impacts on subsidence occurrence. Then land subsidence susceptibility map was prepared in GIS and R environment. The receiver operating characteristic curve was applied to assess the accuracy of random forest algorithm. The area under the curve by value of 0.77 showed that random forest is an acceptable model for land subsidence susceptibility mapping in the study area. The research results can provide a basis for the protection of environment and also promote the sustainable development of economy and society.
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ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-019-8518-3