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تأثیر تغییرات اقلیمی بر تغذیه آب زیرزمینی: بهینه‌سازی فرایندهای بازچرخانی فاضلاب با استفاده از مدل‌های عصبی مصنوعی (ANN) و MODFLOW

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت منابع آب، واحد شهرقدس، دانشگاه آزاد اسلامی، شهرقدس، ایران.

2 دانشیار، گروه مدیریت منابع آب، واحد شهرقدس، دانشگاه آزاد اسلامی، شهرقدس، ایران.

3 استاد، گروه مدیریت منابع آب، واحد شهرقدس، دانشگاه آزاد اسلامی، شهرقدس، ایران.

چکیده
مدیریت کیفیت آب و بهینه‌سازی بازچرخانی فاضلاب، به‌ویژه در شرایط تغییر اقلیم، از چالش‌های مهم منابع آب زیرزمینی است. در این مطالعه، تغییرات پارامترهای کیفیت آب شامل pH، EC، BOD₅، COD و شاخص‌های میکروبی کلی‌فرم و E. coli در مرحله ذخیره‌سازی-بازیابی بازچرخانی فاضلاب، با استفاده از داده‌های یک آزمایش میدانی ۶۰روزه در آبخوان چالوس بررسی شد. این آزمایش شامل تزریق فاضلاب شهری و نمونه‌برداری منظم از کیفیت آب بود. همچنین، یک مدل پرسپترون چندلایه (MLP) با سه لایه پنهان توسعه داده شد و عملکرد آن با مدل عددی MODFLOW مقایسه گردید. شبکه عصبی با استفاده از ۷۰ درصد داده‌ها آموزش و با ۳۰ درصد باقی‌مانده اعتبارسنجی شد و با کاهش حدود ۲۰ درصدی خطای پیش‌بینی نسبت به MODFLOW، عملکرد بهتری نشان داد. برای ارزیابی اثر تغییر اقلیم بر رفتار آبخوان، شرایط اقلیمی دوره ۲۰۳۰ تا ۲۰۵۰ با استفاده از LARS-WG 8 و مدل اقلیمی MRI-ESM2-0، به‌عنوان مناسب‌ترین مدل CMIP6 گزارش‌شده در گزارش ششم IPCC، تحت سناریوهای SSP1-2.6، SSP2-4.5 و SSP5-8.5 تولید شد. نتایج نشان داد تغذیه آبخوان بسته به سناریو ۱۳ تا ۲۵ درصد کاهش می‌یابد و تا سال ۲۰۵۰ افت سطح آب زیرزمینی حدود ۱/۱ تا ۱/۷ متر پیش‌بینی می‌شود. در شرایط بارگذاری ثابت آلاینده‌ها، این افت موجب افزایش ۱۵ تا ۲۸ درصدی غلظت BOD₅، COD، EC، کلی‌فرم و E. coli و در نتیجه کاهش قابل‌توجه کیفیت آب زیرزمینی خواهد شد؛ بنابراین، اجرای راهبردهای سازگار با تغییر اقلیم برای حفاظت از کیفیت آب زیرزمینی ضروری است.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Impact of Climate Change on Groundwater Recharge: Optimizing Wastewater Recycling Processes Using Artificial Neural Networks (ANN) and MODFLOW Models

نویسندگان English

Seyed Behnam Mirfakhraei 1
Hossein Hassanpour Darvishi 2
Seyed Habib Mousavi Jahromi 3
1 PhD. Graduate, Department of Water Resources Management, ShQ.C., Islamic Azad University, Shahr-e Qods, Iran.
2 Associate. Prof, Department of Water Resources Management, ShQ.C., Islamic Azad University, Shahr-e Qods, Iran.
3 Prof. Department of Water Resources Management, ShQ.C., Islamic Azad University, Shahr-e Qods, Iran.
چکیده English

Water quality management and wastewater recycling optimization are critical challenges in groundwater management, especially under climate change. This study investigated key water quality parameters, including pH, EC, BOD5, COD, and microbial indicators such as E. coli and Coliform, during the storage-recovery phase of wastewater recycling. The analysis was based on data from a 60-day field experiment in the Chalous aquifer, where domestic wastewater was injected and water quality was regularly monitored.
To model and predict aquifer behavior, a Multi-Layer Perceptron (MLP) with three hidden layers was developed and compared with the MODFLOW numerical model. The MLP, trained on 70% of the data and validated on the remaining 30%, showed superior predictive performance and reduced modeling errors by about 20% compared with MODFLOW.
The study also assessed the impacts of climate change on groundwater recharge and contamination risk for the period 2030-2050. Future climate conditions were generated using LARS-WG 8 and the MRI-ESM2-0 climate model under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Results showed that groundwater recharge may decrease by 13% to 25%, leading to a projected groundwater level decline of 1.1 to 1.7 m by 2050. Under constant pollutant loading, this decline could increase concentrations of BOD5, COD, EC, Coliform, and E. coli by 15% to 28%, significantly degrading groundwater quality. Overall, the findings emphasize the need for climate-resilient strategies to protect groundwater quality in the future.

کلیدواژه‌ها English

Climate change
groundwater recharge
water quality
ANN
MODFLOW
SSP1-2.6
Alizadeh, M. R., Nikoo, M. R., & Rakhshandehroo, G. R. (2017). Developing a multi-objective conflict-resolution model for optimal groundwater management based on fallback bargaining models and social choice rules: a case study. Water Resources Management, 31(5), 1457-1472. https://doi.org/10.1007/s11269-017-1588-7
An, X. (2022). Responses of water use efficiency to climate change in evapotranspiration and transpiration ecosystems. Ecological Indicators, 141, 109157. https://doi.org/10.1016/j.ecolind.2022.109157
Ansarifard, S., Ghorbanifard, M., Boustani, F., & Abdolazimi, H. (2024). Hydrological simulation and evaluation of drought conditions in the ungauged watershed Parishan lake Iran, using the SWAT model. Journal of Water and Climate Change, 15(9), 4666-4698. https://doi.org/10.2166/wcc.2024.268 
 Bilgiç, G., Bendeş, E., Öztürk, B., & Atasever, S. (2023). Recent advances in artificial neural network research for modeling hydrogen production processes. International Journal of Hydrogen Energy, 48 (50), 18947–18977. https://doi.org/10.1016/j.ijhydene.2023.03.123
Borah, T., Bhattacharjya, R. K. (2015). Development of unknown pollution source identification models using GMS ANN–based simulation optimization methodology. Journal of Hazardous, Toxic, and Radioactive Waste, 19 (3), 04015005. https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000242
Chowdhury, A., Rahnuma, M. (2023). Groundwater contaminant transport modeling using MODFLOW and MT3DMS: A case study in Rajshahi City. Water Practice and Technology, 18 (5), 1255–1272. https://doi.org/10.2166/wpt.2023.076
Chowdhury, T. N., Battamo, A., Nag, R., Zekker, I., & Salauddin, M. (2025). Impacts of climate change on groundwater quality: A systematic literature review of analytical models and machine learning techniques. Environmental Research Letters, 20(3), 033003. https://doi.org/10.1088/1748-9326/abcd1234
Cigizoglu, H. K. (2008). Artificial neural networks in water resources. In Integration of information for environmental security (pp. 115-148). Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6575-0_8
Congalton, R. G. (2001). Accuracy assessment and validation of remotely sensed and other spatial information. International journal of wildland fire, 10(4), 321-328.
Dao, P. U., Heuzard, A. G., Le, T. X. H., Zhao, J., Yin, R., Shang, C., & Fan, C. (2024). The impacts of climate change on groundwater quality: A review. Science of The Total Environment , 912 , 169241. https://doi.org/10.1016/j.scitotenv.2023.169241
Eshtawi, T., Evers, M., Tischbein, B., & Diekkrüger, B. (2016). Integrated hydrologic modeling as a key for sustainable urban water resources planning. Water Research , 101 , 411–428. https://doi.org/10.1016/j.watres.2016.06.005
Espeholt, L., Agrawal, S., Sønderby, C., Kumar, M., Heek, J., Bromberg, C., ... & Kalchbrenner, N. (2022). Deep learning for twelve hour precipitation forecasts. Nature communications, 13(1), 5145. https://doi.org/10.1038/s41467-022-32483-x
Farhadi, S., Nikoo, M. R., Rakhshandehroo, G. R., Akhbari, M., & Alizadeh, M. R. (2023). An agent-basedNash modeling framework for sustainable groundwater management: A case study. Journal AgriculturalWater Management , Volume 177, Pages 348-358. https://doi.org/10.1016/j.agwat.2016.08.018
Fenech, M.; Amaya, I.; Valpuesta, V.; Botella, M.A. Vitamin C content in fruits: Biosynthesis and regulation. Frontiers in Plant Science. 2019, 9, 2006.
Garg, S., & Singh, S. K. (2016). Modeling of arsenic transport in groundwater using MODFLOW: a case study. International Journal of Geomatics and Geosciences, 7(1), 56-81.
Gkika, D. A., Mitropoulos, A. C., Lambropoulou, D. A., Kalavrouziotis, I. K., & Kyzas, G. Z. (2022). Cosmetic wastewater treatment technologies: a review. Environmental Science and Pollution Research, 29(50), 75223-75247. https://doi.org/10.1007/s11356-022-23045-1
Gleeson, T., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (2012).Water balance of global aquifers revealed by groundwater footprint. Nature, 488 (7410), 197–200. https://doi.org/10.1038/nature11295
Gregory, A., Kelly, E., Landa, S., Muthike, D. M., Samo, J., Lopez, J., ... & Cronk, R. (2024). Challenges and opportunities for enhancing groundwater data access and usability in low-and middle-income countries: insights and recommendations from WaSH researchers and practitioners. Journal of Water, Sanitation and Hygiene for Development, 14(10), 929-937. https://doi.org/10.2166/washdev.2024.066
Harbaugh, A. W. (2005). MODFLOW-2005, the U.S. Geological Survey modular groundwater model the ground-water flow process. U.S. Geological Survey Techniques and Methods 6-A16. https://doi.org/10.3133/tm6A16
Houshmand Kouchi Delaram., Esmaili Kazem., Faridhosseini Alireza., Sanaeinejad Seyed Hossein., Khalili Davar., Abbaspour Karim C (2017). Sensitivity of Calibrated Parameters and Water Resource Estimates on Different Objective Functions and Optimization Algorithms. Water, 9, 384. https://doi.org/10.3390/w9060384
IPCC. Climate Change 2021: The Physical Science Basis; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021.
Isık, H., & Akkan, T. (2025). Water quality assessment with artificial neural network models: Performance comparison between SMN, MLP and PS-ANN methodologies. Arabian Journal for Science and Engineering, 50(1), 369-387. https://doi.org/10.1007/s13369-024-09238-5
Kerachian, R., Fallahnia, M., Bazargan-Lari, M. R., Mansoori, A., & Sedghi, H. (2010). A fuzzy game theoretic approach for groundwater resources management: Application of Rubinstein Bargaining Theory. Resources, Conservation and Recycling, 54 (10), 673–682. https://doi.org/10.1016/j.resconrec.2009.11.008
Kløve, B., Ala-Aho, P., Bertrand, G., Gurdak, J. J., Kupfersberger, H., Kværner, J., Muotka, T., Mykrä, H., Preda, E., Rossi, P., Uvo, C. B., Velasco, E., & Pulido-Velazquez, M. (2014). Climate change impacts on groundwater and dependent ecosystems. Journal of Hydrology, 518 (Part B), 250–266. https://doi.org/10.1016/j.jhydrol.2013.06.037
Adhikary, S. (2011). Modeling groundwater flow and salinity intrusion by advective transport in the regional unconfined aquifer of southwest Bangladesh.
La Vigna, F. (2022). Urban groundwater issues and resource management, and their roles in the resilience of cities. Hydrogeology Journal, 30(6), 1657-1683. https://doi.org/10.1007/s10040-022-02517-1
Laoufi, A., Boudjema, A., Guettaia, S., Derdour, A., & Almaliki, A. H. (2024). Integrated simulation of groundwater flow and nitrate transport in an alluvial aquifer using MODFLOW and MT3D: Insights into pollution dynamics and management strategies. Sustainability, 16 (23), 10777. https://doi.org/10.3390/su162310777
Lopez.A.A. PolliceA. LonigroS. Masi,A.M. PaleseG.L. CirelliA. ToscanoR. Passino. (2006).Agricultural wastewater reuse in southern Italy.Desalination.Volume 187, Issues 1–3,  Pages 323-334
Masoumi, F., Najjar-Ghabel, S., & Safarzadeh, A. (2021). Automatic calibration of groundwater simulation model (MODFLOW) by indeterministic SUFI-II algorithm. Amirkabir Journal of Civil Engineering, 53 (4), 345-348. https://doi.org/10.22060/ceej.2019.16990.6426 [In Persian]
Milašinović, M., Ranđelović, A., Jaćimović, N., & Prodanović, D. (2019). Coupled groundwater hydrodynamic and pollution transport modelling using Cellular Automata approach. Journal of Hydrology, 576, 652-666. https://doi.org/10.1016/j.jhydrol.2019.06.062.
Mirzaee, M., Safavi, H. R., Taheriyoun, M., & Rezaei, F. (2021). Multi-objective optimization for optimal extraction of groundwater from a nitrate-contaminated aquifer considering economic-environmental issues: A case study. Journal of contaminant hydrology, 241, 103806. https://doi.org/10.1016/j.jconhyd.2021.103806
Mohammadpour, R., Shaharuddin, S., Zakaria, N. A., Ghani, A. A., Vakili, M., & Chan, N. W. (2016). Prediction of water quality index in free surface constructed wetlands. Environmental Earth Sciences, 75(2), 139.
Nasirabadi, M. S., Khosrojerdi, A., Musavi-jahromi, S. H., & Tabrizi, M. S. (2024). Simulating the climate change effects on the Karaj Dam basin: Hydrological behavior and runoff. Journal of Water and Climate Change, 15 (7), 3094–3114. https://doi.org/10.2166/wcc.2024.12345
Nougadère, A., Sirot, V., Cravedi, J. P., Vasseur, P., Feidt, C., Fussell, R. J., ... & Hulin, M. (2020). Dietary exposure to pesticide residues and associated health risks in infants and young children–results of the French infant total diet study. Environment international, 137, 105529.
Nourani, V., Alami, M. T., & Aminfar, M. H. (2009). A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22 (3), 466–472. https://doi.org/10.1016/j.engappai.2008.09.003
Oruganti, R. K., Katam, K., Show, P. L., Gadhamshetty, V., Upadhyayula, V. K. K., & Bhattacharyya, D. (2022). A comprehensive review on the use of algal-bacterial systems for wastewater treatment with emphasis on nutrient and micropollutant removal. Bioengineered, 13(4), 10412-10453.https://doi.org/10.1080/21655979.2022.2056823
Rahman T. U., Ahsan N. U., Habib A. & Ara A. 2019 Assessment of Saline Water Intrusion in Southwest Coastal Aquifer, Bangladesh Using Visual MODFLOW. In: Advances in
Ranjan, S., Kumar, S., Dutta, S. K., Padhan, S. R., Dayal, P., Sow, S., ... & Bharati, V. (2023). Influence of 36 years of integrated nutrient management on soil carbon sequestration, environmental footprint and agronomic productivity of wheat under rice-wheat cropping system. Frontiers in Environmental Science11, 1222909.
Reichler, T and Kim, J., 2008. How well do coupled models simulate today's climate? Bulletin of the American
Maliva, R. G., & Missimer, T. M. (2010). Aquifer storage and recovery: developing sustainable water supplies. IDA Journal of Desalination and Water Reuse, 2(2), 74-80. https://doi.org/10.1179/ida.2010.2.2.74
Rogers, L. L., Dowla, F. U. (1994). Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resources Research. https://doi.org/10.1029/93WR01494
Ronkanen, A.-K., Kløve, B. (2008). Hydraulics and flow modeling of water treatment wetlands constructed on peatlands in Northern Finland. Water Research, 42 (14), 3826–3836. https://doi.org/10.1016/j.watres.2008.05.008
Rustam, F., Ishaq, A., Kokab, S. T., de la Torre Diez, I., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022). An Artificial Neural Network Model for Water Quality and Water Consumption Prediction. Water, 14 (21), 3359. https://doi.org/10.3390/w14213359
Sahoo, S., Singha, C., Govind, A., & Sharma, P. (2025). Review of aquifer storage and recovery opportunities and challenges in India. Environmental Earth Sciences, 84(5), 122. https://doi.org/10.1007/s12665-025-12124-4
Scanlon, B. R., Longuevergne, L., & Long, D. J. W. R. R. (2012). Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA. Water Resources Research48(4).
Shakeri, R., Nassery, H. R., & Ebadi, T. (2023). Numerical modeling of groundwater flow and nitrate transport using MODFLOW and MT3DMS in the Karaj alluvial aquifer, Iran. Environmental Monitoring and Assessment, 195(1), 242. https://doi.org/10.1007/s10661-022-10881-4
Sharma, N., Zakaullah, M., Tiwari, H., & Kumar, D. (2015). Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Modeling Earth Systems and Environment, 1(3), 23. https://doi.org/10.1007/s40808-015-0027-0
Sihag, P., Singh, B., Sepah Vand, A., & Mehdipour, V. (2020). Modeling the infiltration process with soft computing techniques. ISH Journal of Hydraulic Engineering, 26(2), 138-152. https://doi.org/10.1080/09715010.2018. 1464408
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society
Torres-Martínez, J. A., Mahlknecht, J., Kumar, M., Loge, F. J., & Kaown, D. (2024). Advancing groundwater quality predictions: Machine learning challenges and solutions. Science of The Total Environment, 949, 174973. https://doi.org/10.1016/j.scitotenv.2024.174973
دوره 7، شماره 1
شهریور 1405
صفحه 139-172

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