Evaluation of Subsidence Phenomenon by Multilayer Perceptron Artificial Neural Network (Case Study: Dehgolan Plain, Kurdistan Province, Iran)
Document Type : Research Paper
Authors
- 1 Department of Civil Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Kurdistan, Iran
- 2 Assist. Professor, Department of Civil Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Kurdistan, Iran
- 3 Department of Mapping Engineering, Faculty of Engineering, Khajeh Nasiruddin Tosi University, Tehran, Iran
- 4 Department of Surveying Engineering, Technical and Vocational University, Tehran, Iran
Abstract
Subsidence poses a significant management challenge, causing damage to infrastructure, energy transmission lines, buildings, soil stability, and leading to the formation of sinkholes. This study employed the Multilayer Perceptron (MLP) neural network to evaluate and model the extent of subsidence in the Dehgolan Plain aquifer, located in Kurdistan Province, Iran, between March 23, 2022, and September 24,2023. A subsidence model was constructed using groundwater level data, changes in transmissivity, alluvial thickness, and results from radar interferometry. Regression analysis comparing predicted and observed values confirmed the model's high accuracy in forecasting subsidence. Furthermore, the model successfully estimated missing subsidence rates. The maximum subsidence calculated using radar interferometry over the 552-day period was 154 mm, while the maximum uplift was 16 mm. In comparison, the MLP model estimated a maximum subsidence of 145 mm and a maximum uplift of 12 mm. Subsidence was found to be more pronounced in the western and central regions of the plain compared to the eastern areas. Considering the ongoing progression of subsidence in the Dehgolan Plain aquifer, it is imperative to implement strategies to reduce the over-extraction of groundwater and establish continuous monitoring systems.
Keywords
- Multilayer Perceptron (MLP)
- groundwater level
- Thickness of Alluvium
- Tranmissivity Change
- Land subsidence
Subjects
water resource management
Abrahart, R., & White, S. (2001). Modelling sediment transfer in Malawi: comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(1), 19-24. https:..doi.org.10.1016.S1464-1909(01)85008-5
Ahmadi, S., & Soudmand Afshar, R. (2020). Monitoring of Land Subsidence in Qorveh and Chahardoli Plains of Hamadan and Kurdistan Provinces using PS-InSAR Technique. Environment and Water Engineering, 6(3), 219-233. https:..doi.org.10.22034.jewe.2020.236939.1375
Ali, M. Z., Chu, H.-J., & Burbey, T. J. (2020). Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations. Hydrogeology Journal, 28(8). . https:..doi. rg.10.1007.s10040-020-02211-0
Asghari Moghaddam, A., Nouri Sangarab, S., & Kadkhodaie Ilkhchi, A. (2023). Assessing groundwater vulnerability potential using modified DRASTIC in Ajabshir Plain, NW of Iran. Environmental Monitoring and Assessment, 195(4), 497. https:..doi.org.10.1007.s10661-023-10992-6
Esmaeili, S. , Bahrami, J. , & Kamali, B. (2024). The contributions of natural and anthropogenic climate change on water resources reduction in Zarrinehroud basin of Lake Urmia. Advances in Civil Engineering and Environmental Science, 1(1), 1-14. https://doi.org/10.22034/acees.2024.195339
Banerjee, P., Singh, V., Chatttopadhyay, K., Chandra, P., & Singh, B. (2011). Artificial neural network model as a potential alternative for groundwater salinity forecasting. Journal of Hydrology, 398(3-4), 212-220. https:..doi.org.10.1016.j.jhydrol.2010.12.016
Demuth, H. B., Beale, M. H., De Jess, O., & Hagan, M. T. (2014). Neural network design. Martin Hagan.
Edalat, A., Khodaparast, M., & Rajabi, A. M. (2020). Detecting land subsidence due to groundwater withdrawal in Aliabad Plain, Iran, using ESA sentinel-1 satellite data. Natural Resources Research, 29, 1935-1950. https:..doi.org.10.1007.s11053-019-09546-w
Gharechaee, H., Samani, A. N., Sigaroodi, S. K., Baloochiyan, A., Moosavi, M. S., Hubbart, J. A., & Sadeghi, S. M. M. (2023). Land subsidence susceptibility mapping using Interferometric Synthetic Aperture Radar (InSAR) and machine learning models in a semiarid region of Iran. Land, 12(4), 843. . https:..doi. rg.10.3390.land12040843
Ghahroudi Tali, M., Khodamoradi, F., & Ali Nouri, K. (2023). Effects of groundwater decrease on the of land subsidence in Dehgolan plain, Kurdistan province. Environmental Management Hazards, 10(1), 57-70. 10.22059.JHSCI.2023.359130.777
Hung, W.-C., Hwang, C., Liou, J.-C., Lin, Y.-S., & Yang, H.-L. (2012). Modeling aquifer-system compaction and predicting land subsidence in central Taiwan. Engineering Geology, 147, 78-90.
https://doi.org/10.1016/j.enggeo.2012.07.018
Ku, C.-Y., & Liu, C.-Y. (2023). Modeling of land subsidence using GIS-based artificial neural network in Yunlin County, Taiwan. Scientific Reports, 13(1), 4090. https:..doi.org.10.1038.s41598-023-31390-5
Li, X., Barriot, J. P., Lou, Y., et al. (2023). Towards millimeter-level accuracy in GNSS-based space geodesy: A review of error budget for GNSS precise point positioning. Surveys in Geophysics, 44(6), 1691–1780. https://doi.org/10.1007/s10712-023-09785-w
Hosseinzadeh, E., Anamaghi, S., Behboudian, M., & Kalantari, Z. (2024). Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping. Land, 13(3), 322. https:..doi.org.10.3390.land13030322
Radman, A., & Akhoondzadeh, M. (2022). Monitoring and Modeling of Urmia Lake Area Variations Using Artificial Neural Network. Journal of Environmental Studies, 46(2), 295-317. 10.22059.JES.2021.304189.1008026. [In Persian]
Ranjgar, B., Razavi-Termeh, S. V., Foroughnia, F., Sadeghi-Niaraki, A., & Perissin, D. (2021). Land subsidence susceptibility mapping using persistent scatterer SAR interferometry technique and optimized hybrid machine learning algorithms. Remote Sensing, 13(7), 1326. https:..doi.org.10.3390.rs13071326
Shimosato, K., & Ukita, N. (2021). Multi-modal data fusion for land-subsidence image improvement in PSInSAR analysis. IEEE Access, 9, 141970-141980. https:..doi.org.10.1109.ACCESS.2021.3120133
Wang, Z., Li, L., Yu, Y., Wang, J., Li, Z., & Liu, W. (2021). A novel phase unwrapping method used for monitoring the land subsidence in coal mining area based on U-Net convolutional neural network. Frontiers in Earth Science, 9, 761653. https:..doi.org.10.3389.feart.2021.761653
Zhang, B., Xu, C., Dai, X., & Xiong, X. (2024). Research on mining land subsidence by intelligent hybrid model based on gradient boosting with categorical features support algorithm. Journal of Environmental Management, 354, 120309. http:..dx.doi.org.10.1016.j.jenvman.2024.120309
Zhao, R., Arabameri, A., & Santosh, M. (2024). Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms. Environmental Science and Pollution Research, 31(10), 15443-15466. https:..doi.org.10.1007.s11356-024-32075-w
Zhou, D., Zuo, X., & Zhao, Z. (2022). Constructing a large-scale urban land subsidence prediction method based on neural network algorithm from the perspective of multiple factors. Remote Sensing, 14(8), 1803. https:..doi.org.10.3390.rs14081803
Zhu, X., Zhu, W., Guo, L., Ke, Y., Li, X., Zhu, L., Sun, Y., Liu, Y., Chen, B., & Tian, J. (2023). Study on Land Subsidence Simulation Based on a Back-Propagation Neural Network Combined with the Sparrow Search Algorithm. Remote Sensing, 15(12), 2978. https:..doi.org.10.3390.rs15122978
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