Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Study Design
2.2.1. Causes of Land Subsidence
2.2.2. Interferometric Synthetic Aperture Radar (InSAR)
2.3. Most Determinant LS Susceptibility Variables
2.4. LS Susceptibility Modeling and Validation Map
3. Results and Discussion
3.1. LS Susceptibility Prediction Maps
3.2. Most Determinant LS Susceptibility Variables
3.3. Validation of LS Susceptibility Maps
3.4. Study Implications and Future Directions
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Criteria | Variables |
---|---|
Hydrometeorological | A decline in groundwater level (DGWL), depth of groundwater (DTGW), climate type, flow accumulation, flow direction |
Geological | Lithology, soil type, normalized difference salinity index (NDSI) |
Geomorphological | Elevation, slope, aspect, distance from anticline (DFA), distance from syncline (DFS), distance from salt plain (DFSP), distance from salt lake (DFSL), distance from fault (DFF), Topographic Wetness Index (TWI), stream power index (SPI), curvature, profile curvature, plan curvature |
Anthropogenic | Land use (LU), land cover (LC), distance from a dam (DFD), distance from road (DFR), distance from mine (DFM), distance from a residential area (DFRA) |
Susceptibility Classes | RF | KNN | CART | |||
---|---|---|---|---|---|---|
Area | Area | Area | ||||
km2 | (%) | km2 | (%) | km2 | (%) | |
Very low | 35.68 | 58.13 | 34.09 | 55.54 | 34.09 | 86.80 |
Low | 18.84 | 30.70 | 20.45 | 33.32 | 20.45 | 4.07 |
Moderate | 4.08 | 6.66 | 4.13 | 6.73 | 4.13 | 4.65 |
High | 0.82 | 1.34 | 0.66 | 1.08 | 0.66 | 1.32 |
Very high | 1.93 | 3.14 | 2.05 | 3.33 | 2.49 | 1.62 |
Performance Statistics | Training | Validating | ||||
---|---|---|---|---|---|---|
RF | KNN | CART | RF | KNN | CART | |
Standard deviation (STDEV) | 0.05 | 0.05 | 0.05 | 0.04 | 0.04 | 0.04 |
Root-mean-square error (RMSE) | 0.01 | 0.02 | 0.02 | 0.02 | 0.03 | 0.03 |
Nash–Sutcliffe efficiency (NSE) | 0.95 | 0.91 | 0.81 | 0.76 | 0.69 | 0.71 |
RMSE-observations standard deviation ratio (RSR) | 0.21 | 0.29 | 0.43 | 0.49 | 0.56 | 0.54 |
Correlation coefficient (COR) | 0.98 | 0.96 | 0.90 | 0.88 | 0.83 | 0.85 |
R-squared (R2) | 0.96 | 0.92 | 0.82 | 0.77 | 0.69 | 0.71 |
Kling–Gupta efficiency (KGE) | 0.89 | 0.81 | 0.88 | 0.78 | 0.74 | 0.75 |
Percent bias (PBIAS) | 0.60 | 1.80 | 1.80 | 0.70 | 9.50 | 8.60 |
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Gharechaee, H.; Samani, A.N.; Sigaroodi, S.K.; Baloochiyan, A.; Moosavi, M.S.; Hubbart, J.A.; Sadeghi, S.M.M. Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran. Land 2023, 12, 843. https://doi.org/10.3390/land12040843
Gharechaee H, Samani AN, Sigaroodi SK, Baloochiyan A, Moosavi MS, Hubbart JA, Sadeghi SMM. Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran. Land. 2023; 12(4):843. https://doi.org/10.3390/land12040843
Chicago/Turabian StyleGharechaee, Hamidreza, Aliakbar Nazari Samani, Shahram Khalighi Sigaroodi, Abolfazl Baloochiyan, Maryam Sadat Moosavi, Jason A. Hubbart, and Seyed Mohammad Moein Sadeghi. 2023. "Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran" Land 12, no. 4: 843. https://doi.org/10.3390/land12040843