Abstract
In many parts of the world, especially where surface water resources are rare or not available, groundwater as the largest source of freshwater is used for domestic, agricultural, and industrial water needs. Groundwater use has increased dramatically which has led to groundwater depletion with negative effects. To protect these water resources, their monitoring and management are essential. Long-term groundwater level (GWL) data from individual wells provide the information needed to monitor groundwater depletion locally (for aquifers) and then be compiled into regional and national. One of the most important approaches in groundwater resource management (GRM) is to achieve a suitable model for predicting GWL behaviour or estimating the parameter that affects it. In this study, artificial intelligence approaches include artificial neural networks (ANNs) in two different types of recurrent neural networks (RNNs) and feed-forward neural networks (FNNs), as well as support vector machines (SVMs), used to predict groundwater levels (GWLs) of the Ziveh Aquifer. Parameters including precipitation, temperature, and water levels of seven piezometers with monthly periods have been considered input data, and water levels during the same period have been used as the model’s output for a 14-year statistical period (2005–2018). Root mean square error (RMSE) and correlation coefficient (R2) have been used to study models and compare their efficiency. Despite the inherent potential of each model in the prediction of water levels, the results suggest the relative superiority of SVM compared to ANN. The RMSE results for two SVM training and testing steps were 0.36 and it was equal to 0.38 and 0.41 for FNN and RNN models respectively. To achieve a higher efficiency rate of artificial intelligence models, the use of more dependent variables and different algorithms for future study is recommended.
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References
Abbassi N, MirzaieAtaabadi M, Hasanpour M (2021) Teredolites ichnofacies and its sequences stratigraphy position in the upper part of Ziveh Formation, Moghan area, Eastern Azarbaijan province, northwest Iran. Q J Eng Geol 30(118):15–24. https://doi.org/10.22071/gsj.2020.200272.1700
Abd-Elmaboud ME, Abdel-Gawad HA, El-Alfy KS, Ezzeldin MM (2021) Estimation of groundwater recharge using simulation-optimization model and cascade forward ANN at East Nile Delta aquifer, Egypt. J Hydrol Reg Stud 34:100784. https://doi.org/10.1016/j.ejrh.2021.100784
Alvisi S, Mascellani G, Franchini M, Bardossy A (2005) Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrol Earth Syst Sci 2(3):1107–1145. https://doi.org/10.5194/hess-10-1-2006
Anbari MJ, Zarghami M, Nadiri AA (2021) An uncertain agent-based model for socio-ecological simulation of groundwater use in irrigation: a case study of Lake Urmia Basin Iran. Agric Water Manag 249:106796. https://doi.org/10.1016/j.agwat.2021.106796
Behzad M, Asghari K, Eazi M, Palhang M (2009) Generalization performance of support vector machines and neural networks in runoff modeling. Expert Syst Appl 36(4):7624–7629. https://doi.org/10.1016/j.eswa.2008.09.053
Ben-Daoud M, El Mahrad B, Elhassnaoui I et al (2021) Integrated water resources management: an indicator framework for water management system assessment in the R’Dom Sub-basin. Morocco. Environ Challenges 3:100062. https://doi.org/10.1016/j.envc.2021.100062
Bierkens MFP (1998) Modeling water table fluctuations by means of a stochastic differential equation. Water Resour Res 34(10):2485–2499. https://doi.org/10.1029/98WR02298
Chang J, Wang G, Mao T (2015) Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model. J Hydrol 529(Part 3):1211–1220. https://doi.org/10.1016/j.jhydrol.2015.09.038
Chen Y, Song L, Liu Y, Yang L, Li D (2020) A review of the artificial neural network models for water quality prediction. Appl Sci 10(17):5776. https://doi.org/10.3390/app10175776
Chiang YM, Chang LC, Chang FJ (2004) Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling. J Hydrol 290(3–4):297–311. https://doi.org/10.1016/j.jhydrol.2003.12.033
Coppola E, Poulton M, Charles E, Dustman J, Szidarovszky F (2003) Application of artificial neural networks to complex groundwater management problems Nat. Resour Res 12:303–320. https://doi.org/10.1023/B:NARR.0000007808.11860.7e
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018
Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257. https://doi.org/10.1016/S0022-1694(00)00214-6
Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15(3):208–216. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208)
Gharekhani M, Nadiri A, AsghariMoghaddam A, Sadeghi Aghdam F (2015) Optimization of drastic model using support vector machine and artificial neural network models for assessment of inherent vulnerability of Ardebil Plain’s Aquifer. Ecohydrol 2(3):311–324. https://doi.org/10.22059/IJE.2015.57300
Ghose D, Das U, Roy P (2018) Modeling response of runoff and evapotranspiration for predicting water table depth in arid region using dynamic recurrent neural network. Groundw Sustain Dev 6:263–269. https://doi.org/10.1016/j.gsd.2018.01.007
Gong Y, Zhang Y, Lan S (2016) A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near lake Okeechobee. Florida Water Resour Manag 30:375–391. https://doi.org/10.1007/s11269-015-1167-8
Govindaraju RS (2000) Artificial neural networks in hydrology. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. J Hydrol Eng 5(2):115-137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
Hashemi M (2008) An independent review: the status of water resources in the Lake Uromiyeh basin. Conservation of Iranian wetlands project (CIWP), Department of Environment (DoE), Iran. Newcastle University, UK. https://www.doe.ir/portal/theme/talab/0DB/2-BS/INV/PROD/bs-inv-prod-lu-en-re-2008.pdf
Hong YM (2017) Feasibility of using artificial neural networks to forecast groundwater levels in real time. Landslides 14(5):1815–1826. https://doi.org/10.1007/s10346-017-0844-5
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558. https://doi.org/10.1073/pnas.79.8.2554
Japan International Cooperation Agency (2016) Data collection survey on hydrological cycle of Lake Urmia Basin in the Islamic Republic of Iran. Final report. Ministry of Energy (MOE) and Water Resource Management Company (WRMC). Japan International Cooperation Agency: CTI Engineering International Co., Ltd.: CTI Engineering Co., Ltd.. GE, JR, pp 16–37. https://openjicareport.jica.go.jp/pdf/12266953.pdf
Kouziokas GN, Chatzigeorgiou A, Perakis K (2018) Multilayer feed forward models in groundwater level forecasting using meteorological data in public management. Water Resour Manag 32(15):5041–5052. https://doi.org/10.1007/s11269-018-2126-y
Lohani AK, Krishan G (2015) Application of artificial neural network for groundwater level simulation in Amritsar and Gurdaspur Districts of Punjab, India. J Earth Sci Clim Change 6:4. https://doi.org/10.4172/2157-7617.1000274
Malik A, Bhagwat A (2021) Modelling groundwater level fluctuations in urban areas using artificial neural network Elsevier B V 12:100484. https://doi.org/10.1016/j.gsd.2020.100484
Mohanty S, Jha MK, Kumar A, Panda DK (2013) Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha. India J Hydrol 495:38–51. https://doi.org/10.1016/J.JHYDROL.2013.04.041
Nadiri AA, Fijani E, Tsai FTC, Asghari Moghaddam A (2013) Supervised committee machine with artificial intelligence for prediction of fluoride concentration. J Hydroinformatics 15(4):1474–1490. https://doi.org/10.2166/hydro.2013.008
Nadiri AA, Chitsazan N, Tsai FTC, Moghaddam AA (2014) Bayesian artificial intelligence model averaging for hydraulic conductivity estimation. J Hydrol Eng 19(3):520–532. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000824
Nadiri AA, Shokri S, Tsai FTC, Asghari Moghaddam A (2018) Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model. J Clean Prod 180:539–549. https://doi.org/10.1016/j.jclepro.2018.01.139
Nayak PC, Satyaji Rao YR, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 20(1):77–90. https://doi.org/10.1007/s11269-006-4007-z
Nie S, Bian J, Wan H, Sun X, Zhang B (2017) Simulation and uncertainty analysis for groundwater levels using radial basis function neural network and support vector machine models. J Water Supply Res Technol AQUA 66(1):15–24. https://doi.org/10.2166/aqua.2016.069
Nourani V, Mogaddam AA, Nadiri AO (2008) An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol Process 22(26):5054–5066. https://doi.org/10.1002/hyp.7129
Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22(3):466–472. https://doi.org/10.1016/j.engappai.2008.09.003
Phan TD, Bertone E, Stewart RA (2021) Critical review of system dynamics modelling applications for water resources planning and management. Clean Environ Syst 2:100031. https://doi.org/10.1016/j.cesys.2021.100031
Regional Water Authority of West Azerbaijan Province (2019) Groundwater resources monitoring statistics and reports
Shit PK, Ks V (2021) Geostatistics and geospatial technologies for groundwater resources in India. Hydrogeol. https://doi.org/10.1007/978-3-030-62397-5
Singh VP (2018) Hydrologic modeling: progress and future directions. Geosci Lett 5:15. https://doi.org/10.1186/s40562-018-0113-z
Tang Y, Zang C, Wei Y, Jiang M (2019) Data-driven modeling of groundwater level with least-square support vector machine and spatial-temporal analysis. Geotech Geol Eng 37(3):1661–1670. https://doi.org/10.1007/s10706-018-0713-6
Tayfur G, Nadiri AA, Moghaddam AA (2014) Supervised intelligent committee machine method for hydraulic conductivity estimation. Sustain Water Resour Manag 28(4):1173–1184. https://doi.org/10.1007/s11269-014-0553-y
USGS (2003) Ground-water depletion across the nation. U.S Geological Survey Fact Sheet 103–03. https://pubs.usgs.gov/fs/fs-103-03/JBartolinoFS(2.13.04).pdf
Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138. https://doi.org/10.1016/j.jhydrol.2010.11.002
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The authors acknowledge gratefully the West Azerbaijan Water Authority, for providing data and information of this study.
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Bubakran, K.S., Novinpour, E.A. & Aghdam, F.S. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in the Ziveh Aquifer–West Azerbaijan, NW Iran. Arab J Geosci 16, 287 (2023). https://doi.org/10.1007/s12517-023-11180-z
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DOI: https://doi.org/10.1007/s12517-023-11180-z