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Prediction of groundwater level in the southwest plain of Tehran-Iran by Multiple Modelling (MM) and treating heterogeneity by self-organizing map (SOM) | ||
Geopersia | ||
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 آذر 1403 اصل مقاله (1.72 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/geope.2024.381089.648773 | ||
نویسندگان | ||
Ali Manafiazar1؛ Mashalah Khamechiyan* 1؛ Mohammad reza Nikudel1؛ mohammad sharifikia2 | ||
1Department of Engineering Geology, Faculty of Basic Science, Tarbiat Modares University, Tehran, Iran. | ||
2Department of Remote Sensing (GIS), Faculty of Humanities, Tarbiat Modares University, Tehran, Iran. | ||
چکیده | ||
Groundwater resources are crucial for meeting water supply needs, highlighting the importance of accurate modeling. The study of groundwater level (GWL) fluctuations holds significant implications for various fields such as management studies, engineering design, agricultural practices, and access to high-quality groundwater. With the increasing use of groundwater resources in recent years, there is a growing need for more serious resource management and closer monitoring of consumption.This research utilized three levels to predict fluctuations in groundwater levels. Firstly, the intelligent self-organizing map (SOM) method was used to cluster observation wells (OWs) in order to reduce heterogeneity in hydrogeological environments. Secondly, models including Sugeno fuzzy logic (SFL), recurrent neural network (RNN), and feedforward neural network (FNN) were utilized to predict groundwater level fluctuations based on regional and observational data, including groundwater level data, groundwater abstraction, temperature, and rainfall. Thirdly, the support vector machine (SVM) Artificial Intelligence (AI) strategy was applied to build further understanding, using the results of the second level as input data to improve results. The findings of this study indicate that the SFL model outperforms the other two models at the second level. Additionally, in the third level, the SVM model improved the results, with testing phase accuracies for categories 1, 2, and 3 improving from 0.92, 0.91, and 0.94 to 0.98, 0.96, and 0.99 respectively. | ||
کلیدواژهها | ||
prediction؛ Ground water level؛ SOM؛ AI models؛ Tehran plain | ||
مراجع | ||
Arya Azar, N., Ghordoyee Milan, S., Kayhomayoon, Z., 2021. the prediction of longitudinal dispersion coefficient in natural streams using LS-SVM and ANFIS optimized by Harris hawk optimization algorithm. Journal of Contaminant Hydrology, 240, 103781. https://doi.org/10.1016/j.jconhyd .2021.103781. ASCE, Task Committee on Application of Artificial Neural Networks in Hydrology., 2000. Artificial neural network in hydrology, part I and II Journal of Hydrologic Engineering, 5 (2): 115-137. ASF DAAC, https://search.asf.alaska.edu/. Baghapour, MA., Nobandegani, AF., Talebbeydokhti, N., Bagherzadeh, S., Nadiri, AA., Gharekhani, M., Chitsazan, N., 2016. Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran. Journal of Environmental Health Science and Engineering 14 (1): 13. https://doi.org/10.1186/s40201-016-0254-y. Barzegar, R., Fijani, E., Asghari Moghaddam, A., Tziritis, E., 2017. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Science of the Total Environment, 599: 20-31. Behzad, M, Asghar, K., Eazi, M., Palhang, M., 2009. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications, 36 (4): 7624-7629. https://doi.org/10.1016/j.eswa.2008.09.053. Benaafi, M., Yassin, MA., Usman, AG., Abba, SI., 2022. Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering. GIS, and Statistical Techniques. Sustainability, 14, 2250. https://doi.org/10.3390/su14042250. Buckley, S., 2000. Radar interferometry measurement of land subsidence Ph.D. dissertation. TX: The University of Texas at Austin. http://dx.doi.org/10.26153/tsw/11126. Chen, B., Deng, K., Fan, H., Hao, M., 2013. Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration. International Journal of Mining Science and Technology, 23 (4): 555-61. https://doi.org/10.1016/j.ijmst.2013.07.014. Chen, J., 2019. Satellite gravimetry and mass transport in the Earth system. Geodesy and Geodynamics. 10: 402-415. Geopersia 2025, 15(1): 163-179 177 Coppola, E., Szidarovszky, F., Poulton, M., Charles, E., 2003. Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions. Journal of Hydrologic Engineering, 6: 348- 360. Coulibaly, P., Anctil, F., Bobée, B., 2000. Daily reservoir inflow forecasting using artificial neural networks with a stopped training approach. Journal of Hydrology, 230: 244-257. Daneshmand, F., Adamowski, J., Martel, R., 2023. Regional Groundwater Flow Modeling Using Improved Isogeometric Analysis: Application and Implications in Unconfined Aquifer Systems. Water Resources Management. https://doi.org/10.1007/s11269-023-03631-9. Fijani, E., Nadiri, AA., Moghaddam, AA., Tasi, FT., Dixon, B., 2013. Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer, Iran. Journal of Hydrology, 503: 89-100. Galloway, D., Burbey, T., 2011. Review: regional land subsidence accompanying groundwater extraction, Hydrogeology Journal, 19, 1459- 1486. https://doi.org/10.1007/s10040-011-0775-5. Gholami, V., Khaleghi, MR., Taghvaye Salimi, E., 2020. Groundwater quality modeling using self- organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts. J. Mt. Sci. 17, 1724-1734. https://doi.org/10.1007/s11629-019-5483-y. Haghshenas Haghighi, M., Motagh, M., 2019. Ground surface response to continuous compaction of aquifer system in Tehran, Iran: Results from a long-term multi-sensor InSAR analysis. Remote Sensing of Environment. 221, 534-550. https://doi.org/10.1016/j.rse.2018.11.003. Hamed, Y., Elkiki, M., Gahtani, OS., 2015. Prediction of future groundwater level using artificial neural network, Southern Riyadh, KSA (case study). International Water Technology Journal, 5(2): 149-169. Haselbeck, V., Koedilla, J., Krause, F., Sauter, M., 2019. Self-organizing maps for the identification of groundwater salinity sources based on hydrochemical data. Journal of Hydrology, 576, 610-619. https://doi.org/10.1016/j.jhydrol.2019.06.053. Hopfield, J. J., 1982. Neural network and physical ayatems with emergent collective computational abilities Proceeding of the national academy of sciences of the United States of America, 79: 2554-2558. Huang, Zh., Yuan, X., Sun, S., Leng, G., Tang, Q., 2023. Groundwater Depletion Rate Over China During 1965-2016: The Long-Term Trend and Inter-annual Variation. JGR Atmospheres, 128: 11. https://doi.org/10.1029/2022JD038109. Kohonen, T., Kaski, S., Lappalainen, H., 1997. Self-organized formation of various invariant-feature filters in the adaptive subspace SOM. Neural Computation. 9(6): 1321-1344. Kohonen, T., 1982. Self-organized formation of topologically correct feature maps. Biological cybernetics. 43(1): 59-69. Kumar Chaudhry, A., Kumar, K., Afaq Alam, M., 2019. Spatial distribution of physico-chemical parameters for groundwater quality evaluation in a part of Satluj River Basin, India. Water Supply; 19 (5): 1480-1490. Luo, Q., Perissin, D., Zhang, Y., Jia, YL., 2014. X-Band Multi-Temporal InSAR Analysis of Tianjin Subsidence. Remote Sens, 6: 7933-7951. Mahmodpour, M., Khamechian, M., Nikudel, MR., 2016. Numerical simulation and prediction of regional land subsidence caused by groundwater exploitation in the southwest plain of Tehran, Iran. Engineering Geology 201, 6-28. https://doi.org/10.1016/j.enggeo.2015.12.004. Massonnet, D., Feigl, K., 1998. Radar interferometry and its application to changes in the Earth’s surface. Rev Geophys, 36 (4): 441-500. Manafiazar, A., Khamehchiyan, M., Nadiri, AA., and Sharifikia, M., 2023. Learning simple additive weighting parameters for subsidence vulnerability indices in Tehran plain (Iran) by artificial intelligence methods. European Journal of Environmental and Civil Engineering, 28 (1): 108-127 . Manafiazar, A., Khamehchiyan, M., Nadiri, A.,2019a. Comparison of Vulnerability of the Southwest Tehran Plain Aquifer with Simple Weighting Model (ALPRIFT Model) and Genetic Algorithm (GA). KJES , 4 (2) :199-212 URL: http://gnf.khu.ac.ir/article-1-2665-fa.html [In Persian]. Manafiazar, A., Khamehchiyan, M., Nadiri, A.,2019b. Optimization of the ALPRIFT method using a support vector machine (SVM) to assess the subsidence Vulnerability of the southwestern plain of Tehran. Scientific quarterly journal of Iranian association of engineering geology, 11 (2): 1-14. URL: https://www.jiraeg.ir/article_83263_en.html [In Persian]. 178 Manafiazar et al. Minnig, M., Moeck, C., Radny, D., Schirmer, M., 2018. Impact of urbanization on groundwater recharge rates in Dübendorf. Switzerland. Journal of Hydrology, 563: 1135-1146. Moges, DM., Bhat, HG., Thrivikramji, KP., 2019. Investigation of groundwater resources in highland Ethiopia using a geospatial technology. Model. Earth Syst. Environ. 5: 1333-1345. Mohebbi Tafreshi, G., Nakhaei, M., Lak, RA., 2020. GIS-based comparative study of hybrid fuzzy-gene expression programming and hybrid fuzzy-artificial neural network for land subsidence susceptibility modeling. Stochastic Environmental Research and Risk Assessment, 34: 1059-1087. Mohebbi Tafreshi, G., Nakhaei, M., Lak, R., 2021. Land subsidence risk assessment using GIS fuzzy logic spatial modeling in Varamin aquifer, Iran. GeoJournal, 86: 1203-1223. Nadiri, AA., Chitsazan, N., Frank, TC., Tsai, M., and Asghari Moghaddam, A., 2014. Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation. Journal of Hydrologic Engineering, 19: 520- 532. Nadiri, AA., Sadeghfam, S., Gharekhani, M., Khatibi, R., and Akbari, E., 2018. Introducing the risk aggregation problem to aquifers exposed to impacts of anthropogenic and geogenic origins on a modular basis using ‘risk cell’. Journal of Environmental Management, 217: 654-667. Nadiri, AA., Razzagh, S., Khatibi, R. et al 2021. Predictive groundwater levels modelling by Inclusive Multiple Modelling (IMM) at multiple levels. Earth Sci Inform, 14: 749-763. Naganna, SR., Deka, PC., 2019. Artificial intelligence approaches for spatial modeling of streambed hydraulic conductivity. Acta Geophysica, 67: 891-903. Neshat, A., Pradhan, B., Dadras, M., 2014. Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resources, Conservation and Recycling. 86: 74-86. Nourani, V., Asgharimoghaddam, AA., Nadiri, A., Singh, VP., 2008. Forecasting spatiotemporal water levels of Tabriz aquifer. Trends in Applied Sciences Research 3 (4): 319- 329. Nourani, V., Taghi Alami, M., Daneshvar Vousoughi, F., 2016. Hybrid of SOM-Clustering Method and Wavelet-ANFIS Approach to Model and Infll Missing Groundwater Level Data. Journal of Hydrologic Engineering, 21(9): 05016018. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001398. Panahi, MR., Mousavi, SM., Rahimzadegan, M., 2017. Delineation of groundwater potential zones using remote sensing, GIS, and AHP technique in Tehran-Karaj plain, Iran. Environmental Earth Sciences, 76: 792. https://doi.org/10.1007/s12665-017-7126-3. Pirouzi, A., Eslami, A., 2017. Ground subsidence in plains around Tehran: site survey, records compilation and analysis. International Journal of Geo-Engineering, 8:30. https://doi.org/10.1186/s40703-017-0069-4. Pisner, DA., Schnyer, DM., 2020. Chapter 6 - Support vector machine, Machine Learning, Academic Press, 101-121. https://doi.org/10.1016/B978-0-12-815739-8.00006-7. Rajabi Baniani, S., Chang, L., Maghsoudi, Y., 2021. Mapping and analyzing land subsidence for Tehran using Sentinel-1 SAR and GPS and geological data. EGU General Assembly. https://doi.org/10.5194/egusphere-egu21-295. Rajaee, T., Ebrahimi, H., Nourani, V., 2019. A review of the artificial intelligence methods in groundwater level modeling. Journal of Hydrology, 572: 336-351. Razzagh, S., Sadeghfam, S., Nadiri, AA., 2022. Formulation of Shannon entropy model averaging for groundwater level prediction using artificial intelligence models. International Journal of Environmental Science and Technology. 19: 6203-6220. Rosen, P., 2014. UNAVCO short course: Principles and theory of radar interferometry. Presented at InSAR: An introduction to processing and applications using ISCE and GIAnT, Boulder, CO, 4-6. Rieben, EH., 1955. the geology of the Tehran plain. American Journal of Science 253: 617-639. Roshni, T., Jha, MK., Deo, RC., Vandana, A., 2019. Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System. Water Resources Management, 33: 2381-2397. Sarscape, 2014. https://www.sarmap.ch/index.php/software/sarscape. Samadi, H., Mahmoodzadeh, A., Hussein Mohammed, A et al 2023. Application of Several Fuzzy- Based Techniques for Estimating Tunnel Boring Machine Performance in Metamorphic Rocks. Rock Mechanics and Rock Engineering. https://doi.org/10.1007/s00603-023-03602-x. Samadi, H., Hassanpour, J., Rostami, J., 2023. Prediction of Earth Pressure Balance for EPB-TBM Using Machine Learning Algorithms. International Journal of Geo-Engineering ,14 (11). https://doi.org/10.1186/s40703-023-00198-7. Geopersia 2025, 15(1): 163-179 179 Sharafati, A., Asadollah, SBHS., Neshatd, A., 2020. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. Journal of Hydrology, 591: 125468. https://doi.org/10.1016/j.jhydrol.2020.125468. Shiri, J., Kisib, O., Yoon, H., Lee, KK., Nazemia, AH., 2013. Predicting groundwater level fluctuations with meteorological effect implications A comparative study among soft computing techniques. Computers & Geosciences, 56: 32-44. Sihag, P., 2018. Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Modeling Earth Systems and Environment, 4:189-198. https://doi.org/10.1007/s40808- 018-0434-0. Simmons, BS., Wempen, JM., 2021. Quantifying relationships between subsidence and longwall face advance using DInSAR. International Journal of Mining Science and Technology, 31(1): 91-94. Suykens, JA., De Brabanter, J., Lukas, L., Vandewalle, J., 2002. Weighted least squares support vector machines: robustness and spare approximation. Neurocomputing. 48(1): 85-105. Tomás, R., Romero, R., Mulas, J., Marturià, JJ., Mallorquí, JJ., Lopez-Sanchez, JM., Herrera, G., Gutiérrez, F., González, PJ., Fernández, J., Duque, S., Concha-Dimas, A., Cocksley, G., Castañeda, C., Carrasco, D., Blanco, P., 2014. Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain, Environmental Earth Sciences , 71: 163-181. Vapnik, VN., 1995. the nature of statistical learning theory. Springer. New York. 150 p. ISBN 978-1- 4757-3264-1. https://doi.org/10.1007/978-1-4757-3264-1. Vervoort, A., Declercq, P., 2018. Upward surface movement above deep coal mines after closure and flooding of underground workings. International Journal of Mining Science and Technology; 28: 9-53. Wakode, HB., Baier, K., Jha, R., Azzam, R., 2018. Impact of urbanization on groundwater recharge and urban water balance for the city of Hyderabad. Indihttps://doi.org/10.1016/j.iswcr.2017.10.003. Wempen, J., 2020. Application of DInSAR for short period monitoring of initial subsidence due to longwall mining in the mountain west United States. International Journal of Mining Science and Technology, 30 (1): 23-25. Zhang, Y., 2012. Support Vector Machine Classification Algorithm and Its Application. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_27 | ||
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