Intelligent approach to predict future groundwater level based on artificial neural networks (ANN)
Tóm tắt
To depict hydrogeological variables and understand the physical processes taking place in a complex hydrogeological system, artificial neural networks (ANNs) are widely used as a good alternative approach to tedious numerical models. The aim of this study was to predict the dynamic fluctuations in piezometric levels in Nebhana aquifers (NE Tunisia) using ANNs. A correlation analysis carried out between piezometry, evapotranspiration and rainfall during the period 2000 to 2018 revealed that piezometric levels were influenced by monthly rainfall, evapotranspiration and initial water table level. These informative variables were used as input variables to train the ANN to predict future monthly water table levels for four hydrogeological systems. The minimal and maximal computed relative errors were 0.01 and 19.00%, respectively; root mean square error (RMSE) varied between 0.41 and 2.06; the determination coefficient (R2) ranged between 0.93 and 0.99; and the Nash–Sutcliffe (NASH) efficiency coefficient ranged from 85.32 to 97.82%. To test the generalization capacity of the developed ANN models, we used the ANNs to predict monthly piezometric levels for the period September 2016 to August 2018. The results were satisfactory for all piezometers. Indeed, the minimal and maximal computed RE were − 12.00 and 0.03%, respectively; RMSE was between 0.44 and 1.74; R2 varied between 0.95 and 0.98; the NASH coefficient ranged from 60.00 to 98.99%. These models developed in this study can be adopted for future groundwater level prediction to accurately estimate trends in piezometric levels as well as water pumping costs.
Tài liệu tham khảo
Ahmadi SH, Sedghamiz A (2007) Geostatistical analysis of spatial and temporal variations of groundwater level. Environ Monit Assess 129:277–294
Aichouri I, Hani A, Bougherira N, Djabri L, Chaffai H, Lallahem S (2015) River flow model using artificial neural networks. Energy Procedia 74:1007–1014
Banadkooki BF, Ehteram M, Ahmed NA, Teo YF, Fai CM, Afan HA, Sapitang M, Shafie A (2020) Enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm. Nat Resour Res. https://doi.org/10.1007/s11053-020-09634-2
Bodri L, Cermak V (2000) Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia. Adv Eng Softw 31:311–321
Chitsazan M, Gholamreza R, Neyamdpour A (2015) Forecasting groundwater level by artificial neural networks as an alternative approach to groundwater modeling. J Geol Soc India 85:98–106
Choi DJ, Park H (2001) A hybrid artificial neural network as a software sensor for optimal control of a waste water treatment process. Water Resour 35:3959–3967
Coppola EA, Rana A, Poulton M, Szidarovszky F, Uhl V (2005) A neural network model for predicting water table elevations. Groundwater 43:231–241
Daliakopoulos NI, Coulibaly P, Tsanis KI (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240
Farid I, Trabelsi R, Zouari K, Beji R (2013) Geochemical and isotopic study of surface and groundwater in Ain boumourra basin, central Tunisia. Quat Int 303:210–227
Farid I, Zouari K, Rigane A, Beji R (2015) Origin of the groundwater salinity and geochemical processes in detrital and carbonate aquifers: case of Chougafiya basin (Central Tunisia). J Hydrol 530:508–532
Feng S, Kang S, Huo Z, Chen S, Mao X (2008) Neural networks to simulate regional groundwater level affected by human activities. Groundwater 46:80–90
General Directorate of the Water Ressources, Ministry of Agriculture (2015) Directory of deep aquifers exploitation in Tunisia. Tunis, Tunisia
Ghiassi M, Zimbra DK, Saidane H (2008) Urban water demand forecasting with a dynamic artificial neuronal network model. J Water Resour Plan Manag 134:138–146
Günther F, Fritsch S (2010) Neuralnet: training of neural networks. R J 2:30–38
Hamdi M, Tarhouni J, Zagrarni F, Laaouini G, Muller HS (2017) Assessment of groundwater flow dynamic using GIS tools and 3D geological modeling: case of Sisseb Alem-Nadhour Saouaf basin, Northeastern Tunisia. Int J Innov Appl Stud 19:226–238
Jasmin I, Murali T, Mallikarjuna P (2010) Statistical analysis of groundwater table depths in upper Swarnamukhi river basin. J Water Resour Prot 02(06):577–584
Kandananond K (2011) Forecasting electricity demand in Thailand with an artificial neural network approach. Energies 4:1246–1257
Kaya YZ, Üneş F, Demirci M, Taşar B, Varçin H (2018) Groundwater level prediction using artificial neural network and M5 tree models. Air Water Compon Environ Conf Proc. https://doi.org/10.24193/AWC2018_23
Khan J, Wei J, Ringnér M, Saal L, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu C, Peterson C, Meltzer P (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7:673–679
Klein BD, Rossin DF (1999) Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy. Omega Int J Manag Sci 27:569–582
Lee S, Lee K, Yoon H (2018) Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors. Hydrogeol J 27(2):567–579
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–124
Milan C, Lubomir C (2015) Using R in water resources education. Int J Innov Educ Res 3:97–117
Mosavi A, Edalatifar M (2018) A hybrid neuro-Fuzzy algorithm for prediction of reference evapotranspiration. Recent advances in technology, research and education. Proc international conference on global research and education Enter-Academia., Kaunas, Lithuania, pp 235–2243
Neyamadpour A, Taib S, Abdullah W (2009) Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: a MATLAB application. Comput Geosci 35(11):2268–2274
Ou T, Zhou C (2011) Situation assessment model of civil aviation safety based on neural network and its simulation. J Saf Sci Technol 7:34–41
Shamsuddin MK, Kusin MF, Sulaiman A, Ramli M, Faizal M, Baharuddin T, Adnan S (2016) Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration. MATEC Web Conf 103:04007
Shulaeva AE, Ivanov NA, Uspenskaya NN (2018) Development of artificial neural networks to simulate the process of dichloroethane dehydration in the statistica software program. Proc 14th international scientific technical conference on actual problems of electronic instrument. 44894 2–6 October 2018. Novosibirsik, Russia, pp 280–282
Sun Y, Wendi D, Kim DE, Liong S (2016) Technical note: application of artificial neural networks in groundwater table forecasting—a case study in a Singapore swamp forest. Hydrol Earth Syst Sci 20:1405–1412
Sunitha G, Kumar S, Jyothirani SA, Haragopal VV (2018) Forecasting GDP using ARIMA and artificial neural networks models under Indian environment. Int J Math Trends Technol 56:60–70
Szidarovszky F, Coppola E, Long J, Hall A, Poulton M (2007) A hybrid artificial neural network-numerical model for groundwater problems. Groundwater 45(5):590–600
Taormina R, Chau KW, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intell 25:1670–1676
Turner SWD, Galleli S (2016) Water supply sensitivity to climate change: an R package for implementing reservoir storage analysis in global and regional impact studies. Environ Model Softw 76:13–19
Üneş F, Demirci M, Ispir E, Kaya YZ, Mamak M, Tasar B (2017) Estimation of groundwater level using artificial neural networks: a case study of Hatay-Turkey. In: Environmental engineering. Proc international conference on environmental engineering. 28-27 April 2017. Vilnius, Lithuania, pp 1–6
Üneş F, Bahadırlı ZM, Demirci M, Taşar B, Varçin H, Kaya YZ (2018) Determination of groundwater level fluctuations by artificial neural networks. Nat Eng Sci 3(3):35–42
Üneş F, Demirci M, Taşar B, Kaya YZ, Varçin H (2019) Modeling of dam reservoir volume using generalized regression neural network, support vector machines and m5 decision tree models. Appl Ecol Environ Res 17(3):7043–7055
Uvo CB, Tölle U, Berndtsson R (2000) Forecasting discharge in Amazonia using neural networks. Int Climatol 20:1495–1507
Valtorta M (2006) The effects of data quality on machine learning algorithms. In: Proc 11th international conference on information quality. 10-12 November 2006. Cambridge, USA, pp 485–498