Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as t...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 15; číslo 12; s. 2978
Hlavní autoři: Zhu, Xueqi, Zhu, Wantian, Guo, Lin, Ke, Yinghai, Li, Xiaojuan, Zhu, Lin, Sun, Ying, Liu, Yaxuan, Chen, Beibei, Tian, Jinyan, Gong, Huili
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2023
Témata:
Accuracy
Agricultural production
Algorithms
Artificial intelligence
Back propagation
Back propagation networks
back-propagation neural network
China
Comparative analysis
Compressibility
Computer simulation
Digital mapping
Distribution
Geology
Google Earth Engine
Groundwater
Groundwater data
Groundwater levels
Infrared radiometers
Interferometric synthetic aperture radar
interferometry
Internet
Land subsidence
Learning algorithms
Machine learning
Measurement
multiscale
Neural networks
Optimization
Passeriformes
Radiometry
Remote sensing
Root-mean-square errors
Search algorithms
Sensitivity analysis
Simulation
Simulation models
sparrow search algorithm
Spatial analysis
Spatial data
Subsidence
subsidence simulation
Subsidences (Earth movements)
Synthetic aperture radar
Thickness
time series analysis
water table
Weighting
China
Beijing China
Italy
Mexico
Iran
Indonesia
ISSN:2072-4292, 2072-4292
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Shrnutí:Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15122978