Reliability of Artificial Intelligence-based Models Compared to Numerical Model for Predicting Groundwater Level under Changing Climate
DOI:
https://doi.org/10.52151/jae2024613.1852Keywords:
artificial neural network, climate change, genetic algorithm, groundwater level, hybrid MLP-GA model, MODFLOWAbstract
Groundwater modeling is a crucial tool for simulating groundwater level behavior under future climate change scenarios, and for studying the effects of water management strategies on sustainability of groundwater resources. In this study, two types of models, namely, a physical-based numerical model called MODFLOW, and a data-driven model called Genetic Algorithm-based Multilayer Perceptron (MLP-GA), were evaluated for the reliable predictions of groundwater levels in the semi-arid region of the Karnal district, Haryana. Seven hybrid MLP-GA models were developed with different combinations of input variables such as rainfall, crop evapotranspiration, deep percolation, and irrigation water requirement. The numerical model and hybrid MLP-GA models were calibrated and validated using groundwater-level data from the pre-monsoon period. Among the hybrid models, the model M-1 with four input variables (crop evapotranspiration, rainfall, deep percolation, and applied irrigation water) and 4-29-1 (four input nodes, 29 neurons in the hidden layer, and one output node) model architecture performed the best, but the numerical model showed superiority over the MLP-GA models. The numerical model and M-1 model were used to predict future groundwater levels under projected climate change scenario. According to the numerical model, under the RCP4.5 scenario, groundwater levels in the study area were projected to decline by 7.7 meters by the year 2039 compared to the reference year of 2015. The M-1 model predicted decline of 5.0 meter by the year 2039. The study concluded that all input variables are essential for accurately simulating groundwater levels using MLP-GA models, and that the numerical model is more reliable for assessing the impact of climate change on groundwater behavior during future periods.
References
Adamowski, J., & Chan, H.F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407, 28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013
Adisa, O.M., Botai, J.O., Adeola, A.M., Hassen, A., Botai, C.M., Darkey, D., & Tesfamariam, E. (2019). Application of artificial neural network for predicting maize production in South Africa. Sustainabilty. 11, 1–17. https://doi.org/10.3390/su11041145
Alizamir, M., Kisi, O., & Zounemat-Kermani, M. (2018). Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrological sciences journal, 63(1), 63-73. https://doi.org/10.1080/02626667.2017.1410891
Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop requirements. FAO Irrigation and Drainage Paper No.56. FAO, Rome, Italy.
Anonymous, (2017) . Climate change adaption plan (2015-2020) report. Environment Department, Montreal, Quebec,1-171.
Bailey, R.T., Park, S., Bieger, K., Arnold, J.G., & Allen P.M. (2020). Enhancing SWAT simulation of groundwater flow and groundwater-surface water interactions using MODFLOW routines. Environmental Modelling & Software, 126, 104660. https://doi.org/10.1016/j.envsoft.2020.104660
CGWB, (2006) . Dynamic Groundwater Resources of India (as on March, 2004). Central Ground Water Board (CGWB), Ministry of Water Resources, New Delhi, India.
CGWB, 2013.Ground Water Information Booklet, Karnal District, Haryana. Central Ground Water Board Report (CGWB), Ministry of Water Resources, New Delhi, India.
Chakraborty, S., Maity, P.K., & Das, S. (2020). Investigation, simulation, identification and prediction of groundwater levels in coastal areas of Purba Midnapur, India, using MODFLOW. Environment, Development and Sustainability, 22, 3805–3837. Springer Netherlands. https://doi.org/10.1007/s10668-019-00344-1
Chen, C., He, W., Zhou, H., Xue, Y., Zhu & M. (2020). A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin , northwestern China. Scientfic Reports, 10, 1–13. | https://doi.org/10.1038/s41598-020-60698-9
Coppola, E.A., Rana, A.J., Poulton, M.M., Szidarovszky, F., & Uhl, V.W. (2005). A neural network approach for predicting aquifer water level elevations. Ground Water, 43 (2), 231–241. https://doi.org/10.1111/j.1745-6584.2005.0003.x
Legates, David R., & Gregory J. McCabe. (2013). A refined index of model performance: a rejoinder. International Journal of Climatology, 33, 4: 1053-1056. DOI: 10.1002/joc.3487
Dogan, A., Demirpence, H., & Cobaner, M. (2008). Prediction of groundwater levels from lake levels and climate data using ann approach. Water SA, 34, 199–208. Available on website http://www.wrc.org.za
Doglioni, A., Mancarella, D., Simeone, V., Giustolisi, O., Doglioni, A., Mancarella, D., & Simeone, V. (2010). Inferring groundwater system dynamics from hydrological time-series data data. Hydrological Sciences Journal–Journal des Sciences Hydrologiques, 55(4), 593-608. https://doi.org/10.1080/02626661003747556
Dorigo, M., Maniezzo, V. & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics, part b (cybernetics), 26(1), pp.29-41.
Ehteram, M., Ferdowsi, A., Faramarzpour, M., Al-Janabi, A.M.S., Al-Ansari, N., Bokde, N.D. and Yaseen, Z.M. (2021). Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis. Alexandria Engineering Journal, 60(2), 2193-2208. https://doi.org/10.1016/j.aej.2020.12.034.
Farfani, H., Behnamfar F & Fathollahi, A. (2015). Dynamic analysis of soil-structure interaction using the neural networks and the support vector machines. Expert Systems with Applications, 42(22), 8971-8981. https://doi.org/10.1016/j.eswa.2015.07.053
Esmaeilbeiki, F., Nikpour, M.R., Singh, V.K., Kisi, O., Sihag, P. & Sanikhani, H. (2020). Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables. Journal of Cleaner Production, 276, p.124206. https://doi.org/10.1016/j.jclepro.2020.124206
Ghorbani, M.A., Deo, R.C., Karimi, V., Kashani, M.H. & Ghorbani, S. (2019). Design and implementation of a hybrid MLP-GSA model with multi-layer perceptron-gravitational search algorithm for monthly lake water level forecasting. Stochastic environmental research and risk assessment, 33, 125-147. https://doi.org/10.1007/s00477-018-1630-1
GEC (2009) Ground water resource estimation methodology Report of the Ground Water Resource Estimation Committee, Ministry of water resources government of India, New Delhi. http://cgwb.gov.in/documents/gec97.pdf
Govindaraju, R.S. (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering, 5(2),115-123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115)
Harbaugh, A.W. (2005). MODFLOW-2005, the U.S. Geological Survey modular ground-water model - the Ground-Water Flow Process. U.S. Govt. Print. Off.
Haykin, S. And Network, N. (2004). A comprehensive foundation. Neural Networks. 2 (2004). 41.
Franssen, H.H., Alcolea, A., Riva, M., Bakr, M., Van der Wiel, N., Stauffer, F. & Guadagnini, A. (2009). A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterization of well catchments. Advances in Water Resources, 32(6), 851-872. https://doi.org/https://doi.org/10.1016/j.advwatres.2009.02.011
Holland J.H., (1975). Adaptation in natural and artificial systems. AnnArbor: University of Michigan Press.
Hosseini, Z., Gharechelou, S., Nakhaei, M., & Gharechelou, S. (2016). Optimal design of BP algorithm by ACOR model for groundwater level forecasting: a case study on Shabestar plain. Iran. Arabian Journal of Geosciences. 9 (6), 1-16. https://doi.org/10.1007/s12517-016-2454-2
IPCC. (2013). Climate Change 2013: the physical science basis. Contribution of working
group I to the fifth assessment report of the intergovernmental panel on climate
change. Cambridge University Press. Cambridge, United Kingdom and New York, NY,
USA, pp. 1535.
Jalalkamali, A. & Jalalkamali, N. (2011). Groundwater modeling using hybrid of artificial neural network with genetic algorithm. African Journal of Agricultural Research, 6(26), 5775-5784. DOI: 10.5897/AJAR11.1892
Jalota, S.K., Jain, A.K., & Vashisht, B.B. (2018). Minimize water deficit in wheat crop to ameliorate groundwater decline in rice-wheat cropping system. Agricultural Water Management, 208: 261–267. https://doi.org/10.1016/j.agwat.2018.06.020
Javandel I. & Witherspoon, P.A. (1969). A Method of Analyzing Transient Fluid Flow in Multilayered Aquifers. Water Resource Research, 5, 856-869.
Javandel, I. & Witherspoon, P.A. (1968). Application of the finite element method to transient flow in porous media. Society of Petroleum Engineers Journal, 8(03), 241-252.
Javadinejad, S., Dara, R. & Jafary, F. (2020). Modelling groundwater level fluctuation in an Indian coastal aquifer. Water SA, 46(4), 665-671.
Jha, M.K., & Sahoo, S. (2015). Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater. Hydrological processes, 29(5), 671-691. Hydrological processes, 29(5), pp.671-691.
Jolly, I.D., McEwan, K.L., & Holland, K.L. (2008). A review of groundwater–surface water interactions in arid/semiarid wetlands and the consequences of salinity for wetland ecology. Ecohydrology, 1 (1), 43–58. doi:10.1002/eco.6
Kennedy, J., & Eberhart, RC. (1995). Particle Swarm Optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia; 1942–1948
Kisi, O., Meysam Alizamir, Mohammad Zounemat-Kermani., 2017.Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data.Nat Hazards (2017) 87:367–381.
Kisi, O., Alizamir, M. & Zounemat-Kermani, M. (2017). Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Natural Hazards, 87, 367-381. https://doi.org/10.1007/s11069-017-2767-9
Konikow, L.F., & Kendy, E. (2005). Groundwater depletion : A global problem. Hydrogeology Journal, 13, 317–320. https://doi.org/10.1007/s10040-004-0411-8
Kumar, A., Kumar, P., & Singh, V.K. (2019). Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation, Water Resources Management, 33, 1217-1231. https://doi.org/10.1007/s11269-018-2178-z
Kumar, S., Vivekanand, Narjary, B., Kumar, N., Kaur, S., Yadav, R.K & S.K Kamra. (2020). A GIS-based Methodology for Assigning a Flux Boundary to a Numerical Groundwater Flow Model and Its Effect on Model Calibration. Journal of Geological Society of India, 96, 507–512. https://doi.org/10.1007/s12594-020-1589-7
Kumar, S., Narjary, B., Nand, V., Islam, A., Yadav, R. K., & Kamra, S. K. 2022 Modeling climate change impact on groundwater and adaptation strategies for its sustainable management in the Karnal district of Northwest India. Climatic Change, 173, 3. doi.org/10.1007/s10584-022-03393-0.
Legates, D.R., & McCabe, G.J. (2013). A refined index of model performance: a rejoinder. International Journal of Climatology, 33(4), 1053-1056. DOI: 10.1002/joc.3487
Li H, Sanchez R, Joe Qin S et al (2011) Computer simulation of gas generation and transport in landfills. V: use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning. Chem Eng Sci 66:2646–2659.
Li, H., Sanchez, R., Qin, S.J., Kavak, H.I., Webster, I.A., Tsotsis, T.T. & Sahimi, M. (2011). Computer simulation of gas generation and transport in landfills. V: Use of artificial neural network and the genetic algorithm for short-and long-term forecasting and planning. Chemical engineering science, 66(12), 2646-2659. https://doi.org/10.1016/j.ces.2011.03.013
Liu, W., Bailey, R.T., Andersen, H.E., Jeppesen, E., Park, S., Thodsen, H., Nielsen, A., Molina-, E., & Trolle, D. (2019). Assessing the impacts of groundwater abstractions on flow regimeand stream biota: Combining SWAT-MODFLOW with flow-biota empirical models. Science of Total Environment, 135702. https://doi.org/10.1016/j.scitotenv.2019.135702
Lotfi, E. & Akbarzadeh-T, M.R. (2014). Practical emotional neural networks. Neural Networks, 59, 61-72. https://doi.org/10.1016/j.neunet.2014.06.012
Nourani, V. (2017). An emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology, 544, 267-277. https://doi.org/10.1016/j.jhydrol.2016.11.033
Mall, R.K., Gupta, A., Singh, R., Singh, R.S., & Rathore, L.S. (2006). Water resources and climate change: an Indian perspective. Current Science, 90 (12), 1610–1626.
Malekzadeh, M., Kardar, S. & Shabanlou, S. (2019). Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models. Groundwater for Sustainable Development, 9, 100279. https://doi.org/10.1016/j.gsd.2019.100279
Maroufpoor, S., Maroufpoor, E., Bozorg-Haddad, O., Shiri, J. & Yaseen, Z.M. (2019) Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. Journal of Hydrology, 575,544-556. https://doi.org/10.1016/j.jhydrol.2019.05.045
McCulloch, W. S. & Pitts, W. A. (1943). logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133.
Hagan, M. T., Demuth, H. B., Beale, M. H. and Jesús, O. D. 2014. Neural Network Design.
McDonald, M.G. & Harbaugh, A.W. (1988). A modular three-dimensional finite-difference groundwater flow model. U.S. Govt. Print. Off.
McGarry, K.J., Wermter, S. & MacIntyre, J. (1999). Knowledge extraction from radial basis function networks and multilayer perceptrons. In IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339), 4, 2494-2497. IEEE.
Meysam Alizamira, Ozgur Kisib &Mohammad Zounemat-Kermanic (2018). Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrological Sciences Journal, 63, NO. 1, 63–73. https://doi.org/10.1080/02626667.2017.1410891
Minhas, P.S., Yadav., R.K., K. Lal & R.K. Chaturvedi. (2015). Effect of long-term irrigation with wastewater on growth, biomass production and water use by Eucalyptus (Eucalyptus tereticornis Sm.) planted at variable stocking density. Agricultural Water Management ,152, 151–160. https://doi.org/10.1016/j.agwat.2015.01.009
Mirarabi, A., Nassery, H.R., Nakhaei, M., Adamowski, J., Akbarzadeh, A.H. & Alijani, F. (2019). Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environmental Earth Sciences, 78, 1-15. https://doi.org/10.1007/s12665-019-8474-y
Mirzavand, M., & Ghazavi, R. (2015). A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resources Management, 29, 1315–1328. doi:10.1007/s11269-014-0875-9
Moghaddam, H.K., Moghaddam, H.K., Kivi, Z.R., Bahreinimotlagh, M. & Alizadeh, M.J. (2019). Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundwater for Sustainable Development, 9, 100237. https://doi.org/10.1016/j.gsd.2019.100237
Shariati, H.M. (2014). Groundwater level fluctuation forecasting using artificial neural network in arid and semi-arid environment. Journal of Applied Hydrology, 1(2), 43-53.
Mohanty, S., Jha, M.K., Kumar, A., & Panda, D.K. (2013). Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi – Surua Inter-basin of Odisha , India. Journal of Hydrology, 495, 38–51. https://doi.org/10.1016/j.jhydrol.2013.04.041
Mohanty, S., Jha, M.K., & Raul, S.K. (2015). Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites. Water Resources Management. 29, 5521–5532. https://doi.org/10.1007/s11269-015-1132-6
Nadiri, A.A., Naderi, K., Khatibi, R., & Gharekhani, M. (2019). Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrological sciences journal. 64, 210–226. https://doi.org/10.1080/02626667.2018.1554940
Narjary, B., Kumar, S., Kamra, S. K., Bundela, D. S., & Sharma, D. K. (2015). Impact of rainfall variability on groundwater resources and opportunities of artificial recharge structure to reduce its exploitation in fresh groundwater zones of Haryan. Current Science, 107:1305–1312.
Narjary, B., Kumar, S., Meena, M.D., Kamra, S.K., & Sharma, D.K. (2021). Effects of shallow saline groundwater table depth and evaporative flux on soil salinity dynamics using Hydrus-1D. Agricultural Research, 10:105–115. https://doi.org/10.1007/s40003-020-00484-1
Raes, D., Steduto, P., Hsiao, T., & Fereres, E. (2009). Aquacrop Reference Manual. FAO –
Land and Water Division, Rome, Italy.
Rezaie-balf, M., Raghavendra, S., Ghaemi, A., & Chandra, P. (2017). Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting. Journal of Hydrology, 553, 356–373. https://doi.org/10.1016/j.jhydrol.2017.08.006
Rodell, M., Velicogna, I., & Famiglietti, J.S. (2009). Satellite-based estimates of groundwater depletion in India. Nature, 460 (7258), 999–1002. doi:10.1038/nature08238
Sahoo, S., Russo, T.A., Elliott, J.,& Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, 3878–3895. https://doi.org/10.1002/2016WR019933
Simůnek, J., D. Jacques, M. Šejna, & M.Th. van Genuchten. (2012). The
HP2 program for HYDRUS (2D/3D): A coupled code for simulating
two-dimensional variably-saturated water flow, heat transport, and
biogeochemistry in porous media, version 1.0, PC Progress, Prague,
Czech Republic.
Singh, V. K., Kumar, D., Kashyap, P. S., & Kisi, O. (2018a). Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques. Environ Earth Sciences, 77,1-14. https://doi.org/10.1007/s12665-018-7892-6
Singh, V. K., Singh, B.P., Kisi, O., & Kushwaha, D. (2018b). Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression-based models in arid area, Computers and Electronics in Agriculture,150,205-219. https://doi.org/10.1016/j.compag.2018.04.019
Singh, V.K., Kumar, D., Kashyap, P.S., Singh, P.K., Kumar, A., & Singh, S.K. (2020). Modelling of soil permeability using different data driven algorithms based on physical properties of soil. Journal of Hydrology, 580, 124223. DOI: 10.1016/j.jhydrol.2019.124223
Singh, V.K., Singh, B.P., Kumar, A. & Vivekanand. (2017). A comparative study of artificial intelligence and conventional techniques for rainfall-runoff modeling. International Journal of Agri. Engg, 2, 441-449. DOI10.15740/HAS/IJAE/10.2/441-449
Singh, V.K., Singh., B.P., & Vivekanand. (2016). Basin suspended sediment prediction using soft computing and conventional approaches in India, International Journal of Science and Nature, 7 (2) 459-468.
Soil Conservation Services. (1972). National Engineering Handbooks, Section-4 Hydrology, Washington DC.
Statistical Abstract of Haryana 2015–16. (2017). Department of Economic and Statistical Analysis, Haryana.
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. 8, 348–360. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(348)
Voss, K.A., Famiglietti, J.S., Lo, M., & Linage, C. ( 2013). Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris-Euphrates-Western Iran region. Water Resources Research, 49, 904–914. https://doi.org/10.1002/wrcr.20078
Willmott, C.J., Robeson, S.M., & Matsuur, K. (2011). A refined index of model performance. International Journal of Climatology, 33, 1053-1056. https://doi.org/10.1002/joc.3487
World Water Assessment Program, (2003). The United Nations World Water Development Report 1: Water for People Water for Life. UNESCO, Paris and Berghahn Books, 575 pp.
Yaseen, Z.M., Deo, R.C., Hilal, A., Abd, A.M., Bueno, L.C., Salcedo-Sanz, S. & Nehdi, M.L. (2018). Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Advances in Engineering Software, 115, 112-125. https://doi.org/10.1016/j.advengsoft.2017.09.004
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