A Bibliometric Analysis of Input Parameter in Artificial Neural Network Approach for Groundwater Level Prediction
Abstract
Rapid growth in the Artificial Neural Network (ANN) approach in groundwater level prediction literature calls for an assessment of the trajectory and impacts to identify key themes and future research directions. In this paper, reported a bibliometric analysis of this literature that focuses on examining input paramater uses, focus of research, and research forward. We used Elsevier’s SCOPUS database, Dimensions, and Google Scholar to search for publications from January 2000 to May 2020 on the ANN approach in groundwater level prediction, and analyzed the ?nal sample of 101 publications using RIS file from Mendeley and Vosviewer software tools. Thematic analysis of abstracts revealed a strong focus on groundwater level prediction with artificial neural network approach. The co-occurrence network map showed the hydro-climatology parameter like precipitation, temperature, and groundwater level connected with a large number of frequently used for input in ANN approach, while the evapotranspiration, evaporation, humidity, river stage, runoff parameter demonstrated much weaker links. Re?ected on how these ?ndings may useful for better understand and ultimately be able to use the other hydro-climatology input paramater on groundwater level prediction with artificial neural network approach.
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References
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[17] Wang Y, Traore S, Kerh T. Using Artificial Neural Network for Modeling Suspended Sediment Concentration. Math. Methods Comput. Tech. Electr. Eng., Sofia, Bulgaria: 2008.
[18] Yitian L, Gu RR. Modeling flow and sediment transport in a river system using an artificial neural network. Environ Manage 2003;31:122–34. https://doi.org/10.1007/s00267-002-2862-9.
[19] Ardana PDH. Drought Disaster Forecasting Based On Rainfall Runoff Transformation Modelling. Proceeding Care About Risk Environ., 2015.
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[23] Mirzavand M, Khoshnevisan B, Shamshirband S, Kisi O, Ahmad R, Akib S. Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: a comparative study. Nat Hazards 2015. https://doi.org/10.1007/s11069-015-1602-4.
[24] Khaki M, Yusoff I, Islami N, Hussin NH. Artificial Neural Network Technique for Modeling of Groundwater Level in Langat Basin , Malaysia. Sains Malaysiana 2016;45:19–28.
[25] Wunsch A, Liesch T, Broda S. Forecasting Groundwater Levels using Nonlinear Autoregressive Networks with Exogenous Input (NARX). J Hydrol 2018. https://doi.org/10.1016/j.jhydrol.2018.01.045.
[26] Pasandi M, Salmani N, Samani N. Spatial estimation of water-table depth by artificial neural networks in light of ancillary data. Hydrol Sci J 2017;62:2012–24. https://doi.org/10.1080/02626667.2017.1349908.
[27] Rajaee T, Nourani V, Pouraslan F. Groundwater Level Forecasting Using Wavelet and Kriging. J Hydraul Struct 2016;2:1–21. https://doi.org/10.22055/jhs.2016.12848.
[28] Djurovic N, Domazet M, Stricevic R, Pocuca V, Spalevic V, Pivic R, et al. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS. Sci World J 2015;2015:1–13.
[29] Szidarovszky F, Jr EAC, Long J, Hall AD, Poulton MM. A Hybrid Artificial Neural Network-Numerical Model for Groundwater Problems. Groundwater 2007;45:590–600. https://doi.org/10.1111/j.1745-6584.2007.00330.x.
[30] Ngoie S, Lunda J, Mbuyu A, Makenda G. Overview of Artificial Neural Networks Applications in Groundwater Studies 2018:3768–73.
[31] Coppola Jr. E, Szidarovszky F, Poulton M, Charles E. Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions. J Hydrol Eng 2003;8:348–60.
[32] Trichakis IC, Nikolos IK, Karatzas GP. Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation. Water Resour Manag 2011;25:1143–52. https://doi.org/10.1007/s11269-010-9628-6.
[33] Mohanty S, Jha MK, Raul SK. Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites. Water Resour Manag 2015. https://doi.org/10.1007/s11269-015-1132-6.
[34] Jr AC, Rana AJ, Poulton MM, Szidarovszky F, Uhl VW. A Neural Network Model for Predicting Aquifer Water Level Elevations. Gorund Water 2005;43:231–41.
[35] Daliakopoulos IN, Coulibaly P, Tsanis IK. Groundwater level forecasting using artificial neural networks. J Hydrol 2005;309:229–40. https://doi.org/10.1016/j.jhydrol.2004.12.001.
[36] AK L, G K. Groundwater Level Simulation Using Artificial Neural Network in Southeast, Punjab, India. J Geol Geophys 2015;4:2–7.
[37] Nayak PC, Satyaji Rao YR, Sudheer KP. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 2006;20:77–90. https://doi.org/10.1007/s11269-006-4007-z.
[38] Coulibaly P, Anctil F, Aravena R, Bobée B. Artificial neural network modeling of water table depth fluctuations. Water Resour Res 2001;37:885–96. https://doi.org/10.1029/2000WR900368.
[39] Al-Aboodi AH, Khudhair KM, Al-Aidani AS. Prediction of Groundwater Level in Safwan-Zubair Area Using Artificial Neural Networks. Basrah J Eng Sci 2016;16:42–50.
[40] Lohani AK, Krishan G. Groundwater Level Simulation Using Artificial Neural Network in Southeast, Punjab, India. Geol Geosci 2015;4:3–7. https://doi.org/10.4172/2381-8719.1000206.
[41] Sujatha P, Pradeep Kumar GN. Prediction of Groundwater Levels Using Different Artificial Neural Network Architectures and Algorithms. Nat Environ Pollut Technol 2009;8:429–36.
[42] Coulibaly P, Aravena R, Bobe B. Artificial Neural Network Modeling of Water Table Depth Fluctuations. Water Resour Res 2001;37:885–96. https://doi.org/10.1029/2000WR900368.
[43] Yadav B, CH S, Mathur S, Adamowski J. Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction. J Water L Dev 2017;32:103–12. https://doi.org/10.1515/jwld-2017-0012.
[44] Adamowski J, Chan HF. A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 2011;407:28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013.
[45] Barati R. Discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (2017). ISH J Hydraul Eng 2018;00:1–4. https://doi.org/10.1080/09715010.2018.1488224.
[46] Yoon H, Hyun Y, Ha K, Lee KK, Kim GB. A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions. Comput Geosci 2016;90:144–55. https://doi.org/10.1016/j.cageo.2016.03.002.
[47] Al-aboodi AH, Hassan AA, Munshid HF. A Comparative Study of Adaptive Neuro Fuzzy Inference System and Artificial Neural Networks for Predicting Groundwater Hydraulic Head in an Arid Region A Comparative Study of Adaptive Neuro Fuzzy Inference System and Artificial Neural Networks for Predict. J Univ Babylon Eng Sci 2019;27:317–28.
[48] Kumar S, Singh S. Forecasting Groundwater Level Using Hybrid Modelling Technique. Manag. Nat. Resour. a Chang. Environ., 2015, p. 93–8. https://doi.org/10.1007/978-3-319-12559-6.
[49] Nourani V, Alami MT, Vousoughi FD. Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. J Hydrol 2015;524:255–69. https://doi.org/10.1016/j.jhydrol.2015.02.048.
[50] Zhou T, Wang F, Yang Z. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction. Water (Switzerland) 2017;9:1–21. https://doi.org/10.3390/w9100781.
[51] Guzman SM, Paz JO, Tagert MLM. The Use of NARX Neural Networks to Forecast Daily Groundwater Levels. Water Resour Manag Manag 2017;31:1591–603. https://doi.org/10.1007/s11269-017-1598-5.
[52] Sciences N. Estimation of groundwater level using a hybrid genetic algorithm- neural network. Pollution 2015;1:9–21.
[53] Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015;105:1809–31. https://doi.org/10.1007/s11192-015-1645-z.
[54] &NA; Using Bibliometrics to Support Your Selection of a Nursing Terminology Set. CIN Comput Informatics, Nurs 2009;27:91–2. https://doi.org/10.1097/NCN.0b013e31819ec9ac.
[55] Pereira RS, Santos IC, Oliveira KDS, Leão NCA. Meta-analysis as a research tool: A systematic review of bibliometric studies in administration. vol. 20. 2019. https://doi.org/10.1590/1678-6971/eRAMG190186.
[56] Opejin AK, Aggarwal RM, White DD, Jones JL, Maciejewski R, Mascaro G, et al. A bibliometric analysis of food-energy-water nexus literature. Sustain 2020;12:1–18. https://doi.org/10.3390/su12031112.
[57] Sweileh WM. Bibliometric analysis of peer-reviewed literature in transgender health (1900 - 2017). BMC Int Health Hum Rights 2018;18:1–11. https://doi.org/10.1186/s12914-018-0155-5.
[58] Ding X, Yang Z. Knowledge mapping of platform research: a visual analysis using VOSviewer and CiteSpace. Electron Commer Res 2020. https://doi.org/10.1007/s10660-020-09410-7.
[59] Tanudjaja I, Kow GY. Exploring Bibliometric Mapping in NUS using BibExcel and VOSviewer 2018:1–9.
[60] Armfield NR, Edirippulige S, Caffery LJ, Bradford NK, Grey JW, Smith AC. Telemedicine - A bibliometric and content analysis of 17,932 publication records. Int J Med Inform 2014;83:715–25. https://doi.org/10.1016/j.ijmedinf.2014.07.001.
[61] van Eck NJ, Waltman L. VOSviewer Manual. Leiden: Universiteit Leiden; 2019. https://doi.org/10.3402/jac.v8.30072.
[2] Wu C-Y. Predicting Water Table Fluctuations Using Artificial Neural Network. University of Maryland, 2008.
[3] Bizhanimanzar M, Leconte R, Nuth M. Modelling of shallow water table dynamics using conceptual and physically based integrated surface-water-groundwater hydrologic models. Hydrol Earth Syst Sci 2019;23:2245–60. https://doi.org/10.5194/hess-23-2245-2019.
[4] Park E, Parker JC. A simple model for water table fluctuations in response to precipitation. J Hydrol 2008;356:344–9. https://doi.org/10.1016/j.jhydrol.2008.04.022.
[5] Knotters M, Bierkens MFP. Physical basis of time series models for water table depths. Water Resour Res 2000;36:181–8.
[6] Yang L, Qi Y, Zheng C, Andrews CB, Yue S, Lin S, et al. A modified water-table fluctuation method to characterize regional groundwater discharge. Water (Switzerland) 2018;10:1–16. https://doi.org/10.3390/w10040503.
[7] Yan S feng, Yu S en, Wu Y bai, Pan D feng, Dong J gen. Understanding groundwater table using a statistical model. Water Sci Eng 2018;11:1–7. https://doi.org/10.1016/j.wse.2018.03.003.
[8] Lorenz DL, Delin GN. A regression model to estimate regional groundwater recharge. Groundwater 2007;45:196–208. https://doi.org/10.1111/j.1745-6584.2006.00273.x.
[9] ASCE. Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in Hydrology. II:Hydrologic Application. J Hydrol Eng 2000;5:124–37.
[10] Dharma IS, Putera IA, Putu Doddy Heka Ardana. Artificial Neural Networks Untuk Pemodelan Curah Hujan-Limpasan Pada Daerah Aliran Sungai ( DAS ) Di Pulau Bali. BUMI LESTARI J Environ 2011:9–22.
[11] Abhishek K, Kumar A, Ranjan R, Kumar S. A rainfall prediction model using artificial neural network. Proc - 2012 IEEE Control Syst Grad Res Colloquium, ICSGRC 2012 2012:82–7. https://doi.org/10.1109/ICSGRC.2012.6287140.
[12] Kumar PS, Praveen T V., Prasad MA. Artificial Neural Network Model for Rainfall-Runoff -A Case Study. Int J Hybrid Inf Technol 2016;9:263–72. https://doi.org/10.14257/ijhit.2016.9.3.24.
[13] Tokle HM, Joshi JA. Precipitation (Rainfall) Forecasting Using Artificial Neural Network. Int J Mod Trends Eng Sci 2016;03:91–6.
[14] Ardana PDH, Sudika IGM, Astariani NK, Sumarda G. Application of Feed Forward Backpropagation Neural Network in Monthly Rainfall Prediction. Int J Adv Trends Comput Sci Eng 2019;8:195–200.
[15] Singh KP, Basant A, Malik A, Jain G. Artificial neural network modeling of the river water quality-A case study. Ecol Modell 2009;220:888–95. https://doi.org/10.1016/j.ecolmodel.2009.01.004.
[16] Zaheer I. Application of Artificial Neural Network for Water Quality Management. Lowl Technol Int 2004;5:10–5.
[17] Wang Y, Traore S, Kerh T. Using Artificial Neural Network for Modeling Suspended Sediment Concentration. Math. Methods Comput. Tech. Electr. Eng., Sofia, Bulgaria: 2008.
[18] Yitian L, Gu RR. Modeling flow and sediment transport in a river system using an artificial neural network. Environ Manage 2003;31:122–34. https://doi.org/10.1007/s00267-002-2862-9.
[19] Ardana PDH. Drought Disaster Forecasting Based On Rainfall Runoff Transformation Modelling. Proceeding Care About Risk Environ., 2015.
[20] Porte P, Isaac RK, Kiran K, Mahilang S. Groundwater Level Prediction Using Artificial Neural Network Model. Int J Odf Curr Microbiol Appl Sci 2018;7:2947–54.
[21] Gholami V, Chau KW, Fadaee F, Torkaman J, Ghaffari A. Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. J Hydrol 2015;529:1060–9. https://doi.org/10.1016/j.jhydrol.2015.09.028.
[22] Anandakumar, Senthil Kumar AR, Kale R, Maheshwara Babu B, Sathishkumar U, Srinivasa Reddy G V., et al. A hybrid-wavelet artificial neural network model for monthly water table depth prediction. Curr Sci 2019;117:1475–81. https://doi.org/10.18520/cs/v117/i9/1475-1481.
[23] Mirzavand M, Khoshnevisan B, Shamshirband S, Kisi O, Ahmad R, Akib S. Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: a comparative study. Nat Hazards 2015. https://doi.org/10.1007/s11069-015-1602-4.
[24] Khaki M, Yusoff I, Islami N, Hussin NH. Artificial Neural Network Technique for Modeling of Groundwater Level in Langat Basin , Malaysia. Sains Malaysiana 2016;45:19–28.
[25] Wunsch A, Liesch T, Broda S. Forecasting Groundwater Levels using Nonlinear Autoregressive Networks with Exogenous Input (NARX). J Hydrol 2018. https://doi.org/10.1016/j.jhydrol.2018.01.045.
[26] Pasandi M, Salmani N, Samani N. Spatial estimation of water-table depth by artificial neural networks in light of ancillary data. Hydrol Sci J 2017;62:2012–24. https://doi.org/10.1080/02626667.2017.1349908.
[27] Rajaee T, Nourani V, Pouraslan F. Groundwater Level Forecasting Using Wavelet and Kriging. J Hydraul Struct 2016;2:1–21. https://doi.org/10.22055/jhs.2016.12848.
[28] Djurovic N, Domazet M, Stricevic R, Pocuca V, Spalevic V, Pivic R, et al. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS. Sci World J 2015;2015:1–13.
[29] Szidarovszky F, Jr EAC, Long J, Hall AD, Poulton MM. A Hybrid Artificial Neural Network-Numerical Model for Groundwater Problems. Groundwater 2007;45:590–600. https://doi.org/10.1111/j.1745-6584.2007.00330.x.
[30] Ngoie S, Lunda J, Mbuyu A, Makenda G. Overview of Artificial Neural Networks Applications in Groundwater Studies 2018:3768–73.
[31] Coppola Jr. E, Szidarovszky F, Poulton M, Charles E. Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions. J Hydrol Eng 2003;8:348–60.
[32] Trichakis IC, Nikolos IK, Karatzas GP. Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation. Water Resour Manag 2011;25:1143–52. https://doi.org/10.1007/s11269-010-9628-6.
[33] Mohanty S, Jha MK, Raul SK. Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites. Water Resour Manag 2015. https://doi.org/10.1007/s11269-015-1132-6.
[34] Jr AC, Rana AJ, Poulton MM, Szidarovszky F, Uhl VW. A Neural Network Model for Predicting Aquifer Water Level Elevations. Gorund Water 2005;43:231–41.
[35] Daliakopoulos IN, Coulibaly P, Tsanis IK. Groundwater level forecasting using artificial neural networks. J Hydrol 2005;309:229–40. https://doi.org/10.1016/j.jhydrol.2004.12.001.
[36] AK L, G K. Groundwater Level Simulation Using Artificial Neural Network in Southeast, Punjab, India. J Geol Geophys 2015;4:2–7.
[37] Nayak PC, Satyaji Rao YR, Sudheer KP. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 2006;20:77–90. https://doi.org/10.1007/s11269-006-4007-z.
[38] Coulibaly P, Anctil F, Aravena R, Bobée B. Artificial neural network modeling of water table depth fluctuations. Water Resour Res 2001;37:885–96. https://doi.org/10.1029/2000WR900368.
[39] Al-Aboodi AH, Khudhair KM, Al-Aidani AS. Prediction of Groundwater Level in Safwan-Zubair Area Using Artificial Neural Networks. Basrah J Eng Sci 2016;16:42–50.
[40] Lohani AK, Krishan G. Groundwater Level Simulation Using Artificial Neural Network in Southeast, Punjab, India. Geol Geosci 2015;4:3–7. https://doi.org/10.4172/2381-8719.1000206.
[41] Sujatha P, Pradeep Kumar GN. Prediction of Groundwater Levels Using Different Artificial Neural Network Architectures and Algorithms. Nat Environ Pollut Technol 2009;8:429–36.
[42] Coulibaly P, Aravena R, Bobe B. Artificial Neural Network Modeling of Water Table Depth Fluctuations. Water Resour Res 2001;37:885–96. https://doi.org/10.1029/2000WR900368.
[43] Yadav B, CH S, Mathur S, Adamowski J. Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction. J Water L Dev 2017;32:103–12. https://doi.org/10.1515/jwld-2017-0012.
[44] Adamowski J, Chan HF. A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 2011;407:28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013.
[45] Barati R. Discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (2017). ISH J Hydraul Eng 2018;00:1–4. https://doi.org/10.1080/09715010.2018.1488224.
[46] Yoon H, Hyun Y, Ha K, Lee KK, Kim GB. A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions. Comput Geosci 2016;90:144–55. https://doi.org/10.1016/j.cageo.2016.03.002.
[47] Al-aboodi AH, Hassan AA, Munshid HF. A Comparative Study of Adaptive Neuro Fuzzy Inference System and Artificial Neural Networks for Predicting Groundwater Hydraulic Head in an Arid Region A Comparative Study of Adaptive Neuro Fuzzy Inference System and Artificial Neural Networks for Predict. J Univ Babylon Eng Sci 2019;27:317–28.
[48] Kumar S, Singh S. Forecasting Groundwater Level Using Hybrid Modelling Technique. Manag. Nat. Resour. a Chang. Environ., 2015, p. 93–8. https://doi.org/10.1007/978-3-319-12559-6.
[49] Nourani V, Alami MT, Vousoughi FD. Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. J Hydrol 2015;524:255–69. https://doi.org/10.1016/j.jhydrol.2015.02.048.
[50] Zhou T, Wang F, Yang Z. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction. Water (Switzerland) 2017;9:1–21. https://doi.org/10.3390/w9100781.
[51] Guzman SM, Paz JO, Tagert MLM. The Use of NARX Neural Networks to Forecast Daily Groundwater Levels. Water Resour Manag Manag 2017;31:1591–603. https://doi.org/10.1007/s11269-017-1598-5.
[52] Sciences N. Estimation of groundwater level using a hybrid genetic algorithm- neural network. Pollution 2015;1:9–21.
[53] Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015;105:1809–31. https://doi.org/10.1007/s11192-015-1645-z.
[54] &NA; Using Bibliometrics to Support Your Selection of a Nursing Terminology Set. CIN Comput Informatics, Nurs 2009;27:91–2. https://doi.org/10.1097/NCN.0b013e31819ec9ac.
[55] Pereira RS, Santos IC, Oliveira KDS, Leão NCA. Meta-analysis as a research tool: A systematic review of bibliometric studies in administration. vol. 20. 2019. https://doi.org/10.1590/1678-6971/eRAMG190186.
[56] Opejin AK, Aggarwal RM, White DD, Jones JL, Maciejewski R, Mascaro G, et al. A bibliometric analysis of food-energy-water nexus literature. Sustain 2020;12:1–18. https://doi.org/10.3390/su12031112.
[57] Sweileh WM. Bibliometric analysis of peer-reviewed literature in transgender health (1900 - 2017). BMC Int Health Hum Rights 2018;18:1–11. https://doi.org/10.1186/s12914-018-0155-5.
[58] Ding X, Yang Z. Knowledge mapping of platform research: a visual analysis using VOSviewer and CiteSpace. Electron Commer Res 2020. https://doi.org/10.1007/s10660-020-09410-7.
[59] Tanudjaja I, Kow GY. Exploring Bibliometric Mapping in NUS using BibExcel and VOSviewer 2018:1–9.
[60] Armfield NR, Edirippulige S, Caffery LJ, Bradford NK, Grey JW, Smith AC. Telemedicine - A bibliometric and content analysis of 17,932 publication records. Int J Med Inform 2014;83:715–25. https://doi.org/10.1016/j.ijmedinf.2014.07.001.
[61] van Eck NJ, Waltman L. VOSviewer Manual. Leiden: Universiteit Leiden; 2019. https://doi.org/10.3402/jac.v8.30072.
Published
2020-12-14
How to Cite
ARDANA, Putu Doddy Heka et al.
A Bibliometric Analysis of Input Parameter in Artificial Neural Network Approach for Groundwater Level Prediction.
International Journal of Engineering and Emerging Technology, [S.l.], v. 5, n. 2, p. 131-136, dec. 2020.
ISSN 2579-5988.
Available at: <https://ojs.unud.ac.id/index.php/ijeet/article/view/64982>. Date accessed: 21 nov. 2024.
doi: https://doi.org/10.24843/IJEET.2020.v05.i02.p22.
Section
Articles
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