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The Impact of Climate Change Parameters on Groundwater Level Decline (Case Study: Kuhdasht-Lorestan)

Document Type : Original Article

Authors

1 Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dez.C., Islamic Azad University, Dezful, Iran.

2 Associate Professor, Department of Civil Engineering, Islamic Azad University, Khorramabad branch, Khorramabad, Iran

3 PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Areeo, Khorramabad, Iran

10.22077/jaaq.2025.9841.1119

Abstract

In recent years, global warming, climatic fluctuations, and the pressure of water resource extraction have led to a decline in groundwater levels. Therefore, to prevent the intensification of the above trend and to optimally manage the exploitation of groundwater resources in plains, simulating and predicting groundwater levels is essential and inevitable. To predict groundwater level changes in the Kuhdasht aquifer, meteorological parameters were first predicted and analyzed using the CIMP6 General Circulation Model under various scenarios. Then, using temperature, precipitation, and aquifer extraction data from the 2002-2022 statistical period, the performance of hybrid Artificial Neural Network-Wavelet (WANN) and Artificial Neural Network-Creative Shooter (CSO-ANN) models in estimating groundwater levels was investigated. In the next step, using the selected hybrid model, groundwater level changes in the region were predicted for the 2022-2042 statistical period. The results from the General Circulation Model indicated that with increasing temperature, precipitation decreases. The simulated temperature in all investigated climate models (SSp126, SSP245, and SSP585) for the future period (2022-2042) showed an increase compared to the baseline period in all months, while the average precipitation did not show a clear trend. Furthermore, the modeling results showed that the Artificial Neural Network-Wavelet model performed better than other models in estimating the groundwater level of the Kuhdasht plain. The results from the selected model also indicated a groundwater level decline of 3 to 4.5 meters in this plain during the years 2022-2042.

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References
Abbasian, M., Moghim, S., & Abrishamchi, A. (2019). Performance of the general circulation models in simulating temperature and precipitation over Iran. Theoretical and Applied Climatology, 135, 1465–1483. https://doi.org/10.1007 / s00704-018-2456-y
Affandi, A., Watanabe, K. (2007). Daily groundwater level fluctuation using a soft computing technique. Journal Nature and Science, 5(2), 1–10. https://doi.org/10.1007 / s00521-019-04234-5
Afzaal, H., Farooque, A.A., Abbas, F., Acharya, B., Esau, T. (2019). Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water 12(1),5–23. https://doi.org/10.3390/w12010005
Bahmani, R., Taha, B.M., Ouarda, J. (2021). Groundwater level modeling with hybrid artificial intelligence techniques. Journal of Hydrology, 595, 842-461. https://doi.org/10.1016/j.jhydrol.2020.125659
Bubakran, K.S., Novinpour, E.A. & Aghdam, F.S. (2023). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in the Ziveh Aquifer–West Azerbaijan, NW Iran. Arabian Journal of Geosciences, 16, 287-299. https://doi.org/10.1007/s12517-023-11180-z
Chang, F., Chang, Y. (2006). Adaptive neuro-fuzzy inference system for the prediction of water level in a reservoir. Advances in Water Resources, 1(10), 1–10. https://doi.org/10.1016/j.advwatres.2005.04.015
Dehghani, R., Totabi Poudeh, H., Izadi, Z.(2022). The effect of climate change on groundwater level and its prediction using a modern meta-heuristic model.Groundwater for Sustainable Development,  16(3),  822-845. https://doi.org/10.1016/j.gsd.2021.100702
Dehghani, R. Torabi Poudeh, H., Younesi, H., Shahinejad, B. (2020). Forecasting Daily River Flow Using an Artificial Flora–Support Vector Machine Hybrid Modeling Approach (Case Study: Karkheh Catchment, Iran). Air, Soil, and Water,14, 22-35. https://doi.org/10.1177/1178622120969659
Endo, H., Kitoh, A., Ose, T., Mizuta, R., and Kusunoki, S. (2012). Future changes and uncertainties in Asian precipitation simulated by multiphysics and multi-sea surface temperature ensemble experiments with high-resolution Meteorological Research Institute atmospheric general circulation models (MRI-AGCMs). Journal of Geophysical Research, 117, 244-256. https://doi.org/10.1029/2012JD017874.
Feng, F., Ghorbani, H., Radwan, A. (2024). Predicting groundwater level using traditional and deep machine learning algorithms.Frontiers in Environmental Science. 12(4),525-537. https://doi.org/10.3389/fenvs.2024.1291327
Hornik, K. (1998). Multilayer feed-forward networks are universal approximators. Neural Networks, 2(5), 359–366. https://doi.org/10.1016/0893-6080(89)90020-8
Irwin, S.E., Rubaiya, S., Leanna, M., King, S., Simonovic, P. (2012). Assessment of climatic vulnerability in the Upper Thames River basin: Downscaling with LARS-WG. Water Resources Research Report,12(2),  258-272. https://doi.org/10.4296/cwrj2011-938.
Jahangir, M.H., Haghighi, P., Danehkar, S.H. (2022). Downscaling climate parameters in Fars province using models of the fifth report and RCP scenarios. Ecological Informatics, 68(4),  558-562. https://doi.org/10.1016/j.ecoinf.2022.101558
Jalalkamali, A., JalalKamali, N. (2018). Adaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction of Groundwater Quality Indices: a GIS-based Analysis. Journal of Artificial Intelligence & Data Mining,6(2), 439-445. https://doi.org/10.22044/jadm.2017.1086
Jeong, D.I., Yu, B. & Cannon, A.J. (2023). Climate change impacts on linkages between atmospheric blocking and North American winter cold spells in CanESM2 and CanESM5. Clim Dyn,60, 477–491. https://doi.org/10.1007 / s00382-022-06307-z
Li, T., Zhang, L., & Murakami, H. (2015). Strengthening of the Walker circulation under global warming in an aqua-planet general circulation model simulation. Advances in Atmospheric Sciences, 32,  1473–1480. https://doi.org/10.1007 / s00376-015-5033-7
Meng, X., Liu, Y., Gao, X., & Zhang, H. (2014). A new bio-inspired algorithm: chicken swarm optimization. In International Conference on Swarm Intelligence,8, 86-94. https://doi.org/10.1007/978-3-319-11857-4_10
Mirboluki, A., Mehraein, M., Kisi, O., Kuriqi, A., Barati, R. (2024). Groundwater level estimation using improved deep learning and soft computing methods. Earth Science Informatics, 17, 2587–2608. https://doi.org/10.1007/s12145-024-01300-y
Mirzania, E., Ghorbani, M.A., Asadi, E. (2023). Enhancement of groundwater level prediction using a hybrid ANN-HHO model: a case study (Shabestar Plain in Iran). Arabian Journal of Geosciences. 16(2), 464-482. https://doi.org/10.1007/s12517-023-11584-x
Nakhaei, M., Saberi Nasr, A. (2012a).Predicting groundwater level fluctuations in the Qorveh Plain using a wavelet neural network and comparing it with the MODFLOW numerical model. Advanced Applied Geology, 2(2), 47-58. 10.22059/JGEOPE.2012.29233
Nourani, V., Kisi, Ö., Komasi, M. (2011).Two hybrid artificial intelligence approaches for modeling rainfall–runoff process, Journal of Hydrology, 402(2), 41–59. https://doi.org/10.1016/j.jhydrol.2011.03.002
Nourani, V., Alami, M. T., Aminfar, M.H. (2009). A combined neural-wavelet model for the prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22(2), 466–472. https://doi.org/10.1016/j.engappai.2008.09.003
Rajaee, T., Khani, S., Ravansalar, M. (2022).Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review.Chemometrics and Intelligent Laboratory Systems, 200(3), 1039-1055. https://doi.org/10.1016/j.chemolab.2020.103978
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U., Tompkins, A. (2003). The atmospheric general circulation model ECHAM5, part I: Model description. Max-Planck-Institut für Meteorologie, 349,2-140. https://hdl.handle.net/11858/00-001M-0000-0012-0144-5
Shin, S., Kyung, D., Lee, S., Taik, & Kim, J., and Hyun, J. (2005). An application of support vector machines in a bankruptcy prediction model. Expert Systems with Applications,28(4),127-135. https://doi.org/10.1016/j.eswa.2004.08.009
Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, New York. 8(2), 155-202. https://doi.org/10.1007/978-1-4757-3264-1
Wang, D., Safavi, A.A., and Romagnoli, J.A.(2000). Wavelet-based adaptive robust M-estimator for non-linear system identification, AIChE Journal, 46(4), 1607-1615. https://doi.org/10.1002/aic.690460812
Yang, Q., Song, G.W., Gao, X.D., Lu, Z.Y., Jeon, S.W., Zhang, J. (2023). A random elite ensemble learning swarm optimizer for high-dimensional optimization. Complex & Intelligent Systems, 9(5), 5467–5500. https://doi.org/10.1007/s40747-023-00993-w
Zeidalinejad, N., Dehghani, R.(2023). Use of meta-heuristic approach in the estimation of the aquifer's response to climate change under shared socioeconomic pathways. Groundwater for Sustainable Development, 20(4), 112-132. https://doi.org/10.1016/j.gsd.2022.100882
Zouache, D., Arby, Y. O., Nouioua, F., & Abdelaziz, F. B. (2019). Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems. Computers & Industrial Engineering, 129, 377-391. https://doi.org/10.1016/j.cie.2019.01.055
 
Journal of Aquifer and Qanat
Volume 6, Issue 1 - Serial Number 10
October 2025
Pages 87-106
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