Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Syntheti...
Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer, Iran)
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Publisher
Moscow: Pleiades Publishing
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Language
English
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Publisher
Moscow: Pleiades Publishing
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Contents
Land subsidence, as a dangerous environmental issue, causes serious damages to farms and urban infrastructure. In this regards, this research was conducted with aimed to assess the efficiency of hybrid algorithm Particle Swarm Optimization–Random forest (PSO-RF) for developing land subsidence prediction model. PSO algorithm was used to select the f...
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Full title
Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer, Iran)
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TN_cdi_proquest_journals_2473818033
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2473818033
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ISSN
1028-334X
E-ISSN
1531-8354
DOI
10.1134/S1028334X20090056
