نشریه محیط زیست طبیعی

ارزیابی کارایی روش‌های شبکة عصبی مصنوعی و رگرسیون چند‌متغیره در بررسی افت سطح آبخوان‌های کشور (مطالعة آبخوان دشت اراک)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشگاه آزاد اسلامی واحد اراک، اراک، ایران.

2 گروه کشاورزی و منابع طبیعی، دانشگاه آزاد اسلامی، واحد تربت جام، تربت جام، ایران.

3 گروه منابع طبیعی و محیط زیست، دانشکده فنی مهندسی و کشاورزی، مرکز تحقیقات علوم گیاهی، دانشگاه آزاد اسلامی واحد اراک، اراک، ایران.

10.22059/jne.2023.353826.2518

چکیده

در سال ­های اخیر، منابع آب زیرزمینی دشت اراک تحت تنش شدید قرار گرفته است، به ­طوری­ که در بعضی مناطق به‌علت خشک شدن چاه ­های بهره‌برداری، برای دسترسی به آب اقدام به افزایش عمق چاه کرده ­اند. در بعضی مناطق، سطح آب زیرزمینی بالاست که در آینده، زهدار شدن آن اراضی را در­پی خواهد داشت. برای ساماندهی و سنجش واکنش منابع آب زیرزمینی دشت اراک در مقابل اعمال سناریوهای مختلف مدیریتی و اجرایی از مدل­ سازی منطقه ­ای استفاده شد. هدف این مطالعه، بررسی عوامل مؤثر در عمق سفره ­های آب زیرزمینی به‌منظور ارائة مدل منطقه ­ای با روش رگرسیون چند متغییره برای آبخوان دشت اراک بود. بدین‌منظور عمق متوسط سفره ­های آب زیر زمینی در دشت اراک، به‌عنوان متغییر وابسته و عوامل هدایت آبی تشکیلات آبخوان، ارتفاع، متوسط بارش منطقه، میزان تبخیر و فاصله از منابع آبی به‌عنوان متغیرهای مستقل در نظر گرفته شد و در محیط نرم‌افزار SPSS تجزیة رگرسیونی به‌منظور ارائة یک مدل خطی انجام گرفت. در مرحلة بعد مدل ارائه شده با بکارگیری در مکان ­هایی که از آمار و اطلاعات آن برای ارائة مدل استفاده نشده بود، مورد ارزیابی و کارایی آن مورد بررسی قرار گرفت و در نهایت با بکارگیری این مدل در محیط GIS نقشة عمق سفرة آب زیرزمینی برای منطقة مورد مطالعه تهیه شد. همچنین از شبکة عصبی مصنوعی ANN برای شبیه ­سازی عمق آب زیرزمینی استفاده گردید. عملکرد شبکة عصبی از طریق پارامترهایی چون خطای جذر میانگین مربعات (RMSE) و ضریب همبستگی بین خروجی­های حقیقی و دلخواه (R) سنجیده شد. نتایج حاصل از هر دو روش نشان داد که عوامل نوع قابلیت انتقال تشکیلات آبخوان، افت سفره، توپوگرافی (ارتفاع محل چاه در سطح حوضه آبخیز)، مقادیر بهره ­برداری در حداکثر شعاع عمل چاه و فاصله از منابع آب از عوامل اصلی افت آب زیرزمینی می ­باشند اما کارایی شبکة عصبی مصنوعی در برآورد افت آب زیرزمینی بیشتر از روش رگرسیون چند­متغیره است.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluating the effectiveness of artificial neural network and multivariate regression methods in investigating the drawdown in the level of aquifers (case study: Arak plain aquifer)

نویسندگان [English]

  • Saadat Hanifian 1
  • Mohammad Reza Khaleghi 2
  • Mohsen Najarchi 1
  • Reza Jafarnia 1
  • Javad Varvani 3

1 Department of Water Engineering, Arak Branch, Islamic Azad University, Arak, Iran.

2 Department of Agriculture and Natural Resources, Torbat-e-Jam Branch, Islamic Azad University, Torbat-e-Jam, Iran

3 Department of Natural Resources and Environmental Sciences, Applied Plant Science Research Center, Arak Branch, Islamic Azad University, Arak, Iran.

چکیده [English]

In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, they have increased the depth of wells to access water. In some areas, the groundwater depth (GWD) is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the GWD to provide a regional model with multiple linear regression (MLR) method for Arak plain aquifer. For this purpose, the average GWD in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources  are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GWD map for the region was created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root mean square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GWD drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values ​​at the maximum operating radius of the well, and the distance from water resources are the main factors of GWD drawdown. But the effectiveness of ANN in estimating GWD drawdown is higher than the MLR method.

کلیدواژه‌ها [English]

  • Aquifer formation
  • Artificial neural network
  • GWD drawdown
  • Multiple linear regression
Akbari, M., Jorge, M.R., Madanisadat, H., 2010. Assessment of decreasing of groundwater-table using Geographic Information System (GIS) (Case study: Mashhad Plain Aquifer). Journal of Water and Soil Conservation 16(4), 63-78.
Alipour, Z., 2012. Evaluation of Adaptive Fuzzy-Neural Inference System and Neural Network in Groundwater Level Prediction (North Mahyar Plain). [M.Sc. Thesis]. University of Shahid Chamran, Ahvaz, Iran; 177 p. (in Persian)
Arabameri, A.R., Sohrabi, M., Rezaei, K.H., Yamani, M., Shirani, K., 2018. Simulation of Najaf-Abad watershed groundwater using data driven ensemble model EBF-Index of entropy. Journal of Water and Soil Conservation 25(2), 25-48. (in Persian)
Azari, T., Samani, N., 2018. Modeling the Neuman’s well function by an artificial neural network for the determination of unconfined aquifer parameters. Computational Geosciences 22(4), 1135-1148.
Brunner, P., Kinzelbach, V., 2005. Groundwater Modeling in a remote Chinese Basin- How can models be improved in areas where data are scarce? European Geosciences Union 2005. 2 p.
Chelsea, Q., Wan, Y., 2013. Time series modeling and prediction of salinity in the Caloosahatchee River Estuary. Water Resources Research 49(9), 5804-5816.
Davoodi Moghaddam, D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., Pradhan, B., 2015. Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal Geoscience 8(2), 913-929.
Doll, P., Schmied, H.M., Schuh, C., Portmann, F.T., Eicker, N., 2014. Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites. Water Resources Research 50(7), 5698-5720.
Ghadimi, F., Ghomi‚ M.‚ Azimi‚ R., 2016. Sources of nitrate and bromide contaminants of groundwater in alluvial aquifer of Arak, Iran, Journal of Tethys 4(2), 100-115.
Ghadimi, F., Javadi Sharif, P., 2019. Determination of the source of groundwater pollution in Arak aquifer by stage factor analysis. Journal of Range and Watershed Managment 72(3), 801-818.
Gholami, V., Goli A., Kalteh, A.M., 2015. Modeling sanitary boundaries of drinking water wells on the Caspian Sea southern coasts, Iran. Environmental Earth Sciences 74(4), 2981-2990.
Gholami, V., Khaleghi, M.R., Pirasteh, S., Booij, M.J., 2021. Comparison of self-organizing map, artificial neural network, and co-active neuro-fuzzy inference system methods in simulating groundwater quality: geospatial artificial. Water Resources Management 36, 451-469.
Gholami, V., Sahour, H., 2022. Prediction of groundwater drawdown using artificial neural networks. Environmental Science and Pollution Research 29, 33544-33557.
Golkarian, A., Rahmati, O., 2018. Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran. Environmental Earth Sciences 77, 369.
Gualbert, H.P., Essink, O., 2001. Improving Fresh Groundwater Supply-Problems and Solutions Center of Hydrology (ICHU), Iinstitute of Earth Science, Ocean & Coastal Management. pp. 429-449.
Haghizade, A., Moghaddam, D., Pourghasemi, H., 2017. GIS-based bivariate statistical techniques for groundwater potential analysis. Journal of Earth System Science 126, 109.
Hazbavi, I., Dehghani, R., 2019. Assessment of Intelligent models for Estimating the Electrical Conductivity in Groundwater (Case study: Mazandaran plain). Journal of Environmental Science and Technology 21(1), 87-98.
Ioannis, N., Daliakopoulos, P., Coulibalya, I., Tsanis, K., 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology 309, 229-240.
Kamasi, M., Sharghi, S., Nourani, V., 2016. Identification of Factors Affecting Groundwater Level Reduction Using Wavelet-Entropy Criterion (Case Study: Silakhor Plain Aquifer). Hydrogeomorphology 9(4), 63-86. (in Persian)
Karimiyan, A., Egdernezhad, A., 2021. Simulation of Groundwater Level and Salinity in Ramhormoz Plain Using Artificial Neural Network Model and Optimized Artificial Neural Network Model. Iranian Journal of Research in Environmental Health, Spring 7(1), 17-26.
Keykhosravi, S.S., Nejadkoorki, F., Amintoosi, M., 2019. Estimation of Artificial Neural Networks Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory. Journal of Research in Environmental Health 5(1), 43-52.
Krishna, B., Satyaji Rao, Y.R., Vijaya, T., 2008. Modeling groundwater levels in an urban coastal aquifer using artificial neural networks. Journal of Hydrological Process 22, 1180-1188.
Lallahem, S., Mania, J., Hani, A., Najjar, Y., 2005. On the use of neural networks to evaluate groundwater levels in fractured media. Journal of Hydrology 307(1-4), 92-111.
Lee, S., Hyun, Y., Lee, M., 2019. Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea. Sustainability 11, 1678.
Mirsanjari, M., Mohammadyari, F., Basiri, R., Hamidi pour, F., 2017. Modeling quality parameters EC, SAR and TDS in groundwater using artificial neural network (case study: Mehran Plain and DEHLORAN). Human & Environment 15(3), 1-12.
Mirzavand, M., Ghasemieh, H., Sadatinejad, S., Akbari, M., 2015. Comparison of Artificial Neural Network (ANN) and Multi Variable Regression Analysis (MRA) Models to Predict Ground Water Quality Changes (Case Study: Kashan Aquifer. Water and Soil Science 25(2), 207-220.
Moradi Farahabadi, M., Habibnejad Roshan, M., Wahabzadeh, G., 2013. Examination and simulation of underground water level fluctuations using artificial neural network (case study: Sari Neka coastal aquifer), 5th Iranian Water Resources Management Conference, Tehran, https://civilica.com/doc/269067.
Nofal, E.R., Amer, M.A., El-Didy, S.M.  Fekry, A.M., 2015. Delineation and modeling of seawater intrusion into the Nile Delta Aquifer: a new perspective. Water Science 29(2), 156-166.
Nordqvist, R., Gustafsson, E., Andersson, P., Thur, P., AB, G., 2008. Groundwater flow and hydraulic gradients in fractures and fracture zones at Forsmark and Oskarshamn. SKB Rapport. pp. 1-69.
Priyanka, B.N., Mahesha, A., 2015. Parametric studies on saltwater intrusion into coastal aquifers for anticipate sea level rise. Aquatic Procedia 4, 103-108.
Piri, H., Bameri, A., 2014. Estimation of Sodium Absorption Ration (SAR) in Groundwater Using the Artificial Neural Network and Linear Multiple Regression: Case Study: The Bajestan Plain. Water Resources Engineering 7(21), 67-80.
Poormohammadi, S., dastorani, M.T., Jafari, H., Rahimian, M.H., Goodarzi, M., Mesmarian, Z., Baqeri, F. 2016. The groundwater balance analysis in Tuyserkan-Hamedan plain, by using the mathematical model MODFLOW. The Journal of Echohydrology 2(4), 371-382. (in Persian)
Rahmati, O., Naghibi, S.A., Shahabi, H., Bui, D.T., Pradhan, B., Azareh, A., et al., 2018. Groundwater spring potential modeling: Comprising the capability and robustness of three different modeling approaches. Journal of Hydrology 565, 248-261.
Roshni, T., Jha, M.K., Deo, R.C., 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, pp. 1-17.
Sahour, H., Gholami, V., Vazifedan, M., 2020. A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer. Journal of Hydrology 591, 125321.
Samani, N., Gohari-Moghadam, M., Safavi, A.A. 2007. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology 340, 1-11.
Solomatine, D.P., Ostfeld, A., 2008. Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics 10(1), 3-22.
Vaheddoost, B., Aksoy, H., 2018. Interaction of groundwater with Lake Urmia in Iran. Hydrological Processes 32(21), 3283-3295.
Zare, M., Ghafouri, H., Safavi, H., 2021. Comparative Evaluation of Numerical Model and Artificial Neural Network for Quantity and Quality Simulation of Najafabad Aquifer. Water and Soil Science 31(1), 75-87.
Zhang, M., 2001. Information-Statistics evaluation on the effects of ground water buried depth to upper soil and groundwater salinity, China postdoctoral preceding science press, Beijng, China, pp. 221-224.
نشریه محیط زیست طبیعی
دوره 76، شماره 3
مهر 1402
صفحه 399-412