Aderemi, B., Olwal, T., Ndambuki, J., and Rwanga, S., 2022, A review of groundwater management models with a focus on IoT-Based systems, Sustainability, 14(1), 148.
10.3390/su14010148Abd El-Aziz, A.A., Alsalem K.O., and Mahmood, M.A, 2021, An intelligent groundwater management recommender system, Indian J. Sci. Technol., 14(37), 2871-2879.
10.17485/IJST/v14i37.1332Ahn, E., 2021, Analysis of digital twin technology trends related to geoscience and mineral resources after the Korean new deal policy in 2020, Econ. Environ. Geol., 54(6), 659-670.
10.9719/EEG.2021.54.6.659Ahn, J.J., Kim, Y.M., Yoo, K., Park, J., and Oh, K.J., 2012, Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability, Environ. Monit. Assess., 184, 6637-6645.
10.1007/s10661-011-2448-1Amini, M., Abbaspour, K., and Johnson, C., 2010, A comparison of different rule-based statistical models for modeling geogenic groundwater contamination, Environ. Model. Softw., 25(12), 1650-1657.
10.1016/j.envsoft.2010.05.014Bowes, B.D., Sadler, J.M., Morsy, M.M., Behl, M., and Good-all, J.L., 2019, Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks, Water, 11(5), 1098.
10.3390/w11051098Chang, H., Moon, B., Yoon, S., and Jin, T., 2017, Development and performance evaluation of multiple sensor for groundwater quality monitoring and remote control system using IoT, J. Korea Inst. Inf. Commun. Eng., 21(10), 1957-1963.
Chen, C., He, W., Zhou, H., Xue, Y., and Zhu, M., 2020, A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China, Scientific Reports, 10, 3904.
10.1038/s41598-020-60698-932127583PMC7054559Coulibaly, P., Anctil, F., and Bobée, B., 2000, Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J. Hydrol., 203(3-4), 244-257.
10.1016/S0022-1694(00)00214-6Coulibaly, P., Anctil, F., and Bobee, B., 2001a, Multivariate reservoir inflow forecasting using temporal neural networks, J. Hydrol. Eng., 6(5).
10.1061/(ASCE)1084-0699(2001)6:5(367)Coulibaly, P., Anctil, F., Aravena, R., and Bobee, B., 2001b, Artificial neural network modeling of water table depth fluctuations, Water Resour. Res., 37(4), 885-896.
10.1029/2000WR900368Coyte, R.M., Singh, A., Furst, K.E., Mitch, W.A., and Vengosh, A., 2019, Co-occurrence of geogenic and anthropogenic contaminants in groundwater from Rajasthan, India. Science of the Total Environment, 688, 1216-1227.
10.1016/j.scitotenv.2019.06.334Cristaldi, L., Ferrero, A., Macchi, M., Mehrafshan, A., and Arpaia, P., 2020, Virtual Sensors: a Tool to Improve Reliability, 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, 10.1109/MetroInd4.0IoT48571.2020.9138173.
10.1109/MetroInd4.0IoT48571.2020.9138173Daliakopoulos, I., Coulibaly, P., and Tsanis, I., 2005, Groundwater level forecasting using artificial neural networks, J. Hydrol., 309(1-4), 229-240.
10.1016/j.jhydrol.2004.12.001Drage, J. and Kennedy, G., 2020, Building a low-cost, internet-of-things, real-time groundwater level monitoring Network, Ground Water Monit. Remediat., 40(4), 67-73.
10.1111/gwmr.12408Edmunds, W.M., Shand, P., Hart, P., and Ward, R.S., 2003, The natural (baseline) quality of groundwater: a UK pilot study, Science of the Total Environment, 310(1-3), 25-35.
10.1016/S0048-9697(02)00620-4Frederick, L., VanDerslice, J., Taddie, M., Malecki, K., Gregg, J., Faust, N., and Johnson, W., 2016, Contrasting regional and national mechanisms for predicting elevated arsenic in private wells across the United States using classification and regression trees, Water Research, 91, 295-304.
10.1016/j.watres.2016.01.023Goodall, J., Horsburgh, J., Whiteaker, T., Maidment, D., and Zaslavsky, I., 2008, A first approach to web services for the National Water Information System, Environ. Model. Softw., 23(4), 404-411.
10.1016/j.envsoft.2007.01.005Hussein, E., Thron, C., Ghaziasgar, M., Bagula, A., and Vaccari, M., 2020, Groundwater prediction using machine-learning tools, Algorithms, 13(11), 300.
10.3390/a13110300Jeong, J. and Park, E., 2019, Comparative applications of data-driven models representing water table fluctuations, J. Hydrol., 572, 261-273.
10.1016/j.jhydrol.2019.02.051Jeong, J., Park, E., Chen, H., Kim, K., Han, W., and Suk, H., 2020a, Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data, J. Hydrol., 582 124512.
10.1016/j.jhydrol.2019.124512Jeong, J., Jeong, J., Park, E., Lee, B., Song, S., Han, W., and Chung, S., 2020b, Development of an efficient data-driven method to estimate the hydraulic properties of aquifers from groundwater level fluctuation pattern features, J. Hydrol., 590, 125453.
10.1016/j.jhydrol.2020.125453Jeong, J., Jeong, J., Lee, B., and Song, S., 2021, The applicability of conditional generative model generating groundwater level fluctuation corresponding to precipitation pattern, Econ. Environ. Geol., 54(1), 77-89.
10.9719/EEG.2021.54.1.77Jeong, J., Choung, S., Jeong, D., Kim, M., Kim, H., and Kim, J., 2022, Development of data-driven models for estimating the probability of high-concentration occurrence of naturally occurring radioactive materials in groundwater, J. Hydrol., 605, 127346.
10.1016/j.jhydrol.2021.127346Kenda, K., Cerin, M., Bogataj, M., Senozetnik, M., Klemen, K., Pergar, P., Laspidou, C., and Mladenic, D., 2018, Groundwater modeling with machine learning techniques: ljubljana polje aquifer, Proceedings, 2(11), 697.
10.3390/proceedings2110697Kim, G., Hwang, C., Shin, H., and Choi, M, 2019a, Applicability of groundwater recharge rate estimation method based on artificial neural networks in unmeasured areas, J. Geol. Soc. Korea, 55(6), 693-701.
10.14770/jgsk.2019.55.6.693Kim, G., Kim, J., and Shin, H., 2019b, Estimation of groundwater usage for the living (domestic and business) purpose wells by using a regression tree method, J. Geol. Soc. Korea, 55(6), 683-691.
10.14770/jgsk.2019.55.6.683Kim, I. and Lee, J., 2021, Performance analysis of ANN prediction for groundwater level considering regional‐specific influence components, Groundwater, 60(3), 344-361.
10.1111/gwat.13156Kim, Y., Jeong, J., Park, H., Kwon, M., Cho, C., and Jeong, J., 2022, Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data, J. Hydrol., 612, 128127.
10.1016/j.jhydrol.2022.128127Kong, Q., 2021, Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts, Lawrence livermore national laboratory, LLNL-TR-830065.
10.2172/1836932Kulabako, N.R., Nalubega, M., and Thunvik, R., 2007, Study of the impact of land use and hydrogeological settings on the shallow groundwater quality in a peri-urban area of Kampala, Uganda, Science of the Total Environment, 381(1-3), 180-199.
10.1016/j.scitotenv.2007.03.035Kundzewicz, Z. (Ed.)., 1995, New Uncertainty Concepts in Hydrology and Water Resources (International Hydrology Series). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511564482.
10.1017/CBO9780511564482Lebron, L., Schaap, M.G., and Suarez, D.L., 1999, Saturated hydraulic conductivity prediction from microscopic pore geometry measurements and neural networks analysis, Water Resour. Res., 35(10), 3149-3158.
10.1029/1999WR900195Lee, J., Kang, S., Kim, T., and Chun, G., 2018, Development of groundwater level monitoring and forecasting technique for drought analysis (II) – Groundwater drought forecasting using SPI, SGI and ANN, J. Korea Water Resour. Assoc., 51(11), 1021-1029.
Lee, S., Hyun, Y., and Lee, M., 2019, Groundwater potential mapping using data mining models of big data analysis in Goyang-si, South Korea, Sustainability, 11(6), 1678.
10.3390/su11061678Lee, S., Jeong, J., Kim, M., Park, W., Kim, Y., Park, J., Park, H., Park, G., and Jeong, J., 2021, Data-driven analysis for developing the effective groundwater management system in Daejeong-Hangyeong watershed in Jeju Island, Econ. Environ. Geol., 54(3), 373-387.
10.9719/EEG.2021.54.3.373Liu, L., Kuo, S.M., and Zhou, M., 2009, Virtual sensing techniques and their applications, 2009 International Conference on Networking, Sensing and Control, 31, doi:10.1109/ICNSC.2009.4919241
10.1109/ICNSC.2009.4919241Majumdar, S., Smith, R., Butler Jr, J.J., and Lakshmi, V., 2020, Groundwater withdrawal prediction using integrated multitem-poral remote sensing data sets and machine learning, Water Resour. Res., 56(11), 11.
10.1029/2020WR028059Malakar, P., Mukherjee, A., Bhanja, S.N., Ray, R. K., Sarkar, S., and Zahid, A., 2021, Machine-learning-based regional-scale groundwater level prediction using GRACE, Hydrogeol., 29(3), 1027-1042.
10.1007/s10040-021-02306-2Najafabadipour, A., Kamali, G., and Nezamabadi-pour, H., 2022, Application of artificial intelligence techniques for the determination of groundwater level using spatio-temporal parameters, ACS Omega, 7(12), 10751-10764. https://doi.org/10.1021/acsomega.2c00536. 2c00536.
10.1021/acsomega.2c0053635382324PMC8973156National Water Information System: Mapper, https://toolkit.climate.gov/tool/national-water-information-system-mapper, 2022-11-17.
NASA, 2019, Adavanced Information Systems Technology (AIST) Program, https://esto.nasa.gov/project-selections-for-aist-18/.
Nevo, S., Morin, E., Rosenthal, A.G., Metzger, A., Barshai, C., Weitzner, D., Voloshin, D., Kratzert, F., Elidan, G., Dror, G., Begelman, G., Nearing, G., Shalev, G., Noga, H., Shavitt, I., Yuk\-lea, L., Royz, M., Giladi, N., Levi, N.P., Reich, O., Gilon, O., Maor, R., Timnat, S., Shechter, T., Anisimov, V., Gigi, Y., Levin, Y., Moshe, Z., Ben-Haim, Z., Hassidim, A., and Matias, Y., 2021, Flood forecasting with machine learning models in an operational framework, Hydrol. Earth Syst. Sci., 26, 4013-4032. https://doi.org/10.5194/hess-26-4013-2022
10.5194/hess-26-4013-2022Neyens, D., Baisset, M., and Lovighi, H., 2018, Monitoring the groundwater quality/quantity from your desktop – application to salt water intrusion monitoring EMI: Environmental data Management Interface, E3S Web Conf., 54, 00021.
10.1051/e3sconf/20185400021Nolan, B., Gronberg, J., Faunt, C., Eberts, S., and Belitz, K., 2014, Modeling nitrate at domestic and public-supply well depths in the central Valley, California, Environ. Technol., 48(10), 5643-5651.
10.1021/es405452qNolan, B., Fienen, M., and Lorenz, D., 2015, A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA, J. Hydrol., 531, 902-911.
10.1016/j.jhydrol.2015.10.025Nova Scotia Real-Time Shallow Aquifer Monitoring Network, https://novascotia.ca/natr/meb/water-resources/aquifer-network.asp, 2022-11-21.
Paepae, T., Bokoro, P.N., and Kyamakya, K., 2021, From fully physical to virtual sensing for water quality assessment: a comprehensive review of the relevant state-of-the-art, Sensors, 21(21), 6971, https://doi.org/10.3390/s21216971.
10.3390/s2121697134770278PMC8587795Park, S. and Jeong, G, 2021, Variation of seasonal groundwater recharge analyzed using Landsat-8 OLI data and a CART algo-rithm, Eng. Geol., 31(3), 395-432.
Podgorski, J., Labhasetwar, P., Saha, D., and Berg, M., 2018, Prediction modeling and mapping of groundwater fluoride contamination throughout India, Environ. Sci. Technol., 52(17), 9889-9898.
10.1021/acs.est.8b01679Podgorski, J., Araya, D., and Berg, M., 2022, Geogenic managanese and iron in groundwater of Southeast Asia and Bangla-desh – Machine learning spatial prediction modeling and comparison with arsenic, Sci. Total Environ., 833, 155131.
10.1016/j.scitotenv.2022.155131Ransom, K.M., Nolan, B.T., Stackelberg, P.E., Belitz, K., and Fram, M.S., 2022, Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States, Sci. Total Environ., 807, 151065.
10.1016/j.scitotenv.2021.151065Rizzo, D.M. and Dougherty, D.E., 1994, Characterization of aquifer properties using artificial neural networks: Neural kriging, Water Resour. Res., 302(2), 483-497.
10.1029/93WR02477Sahoo, S., Russo, T., Elliott, J., and Foster, I., 2017, Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S., Water Resour. Res., 53(5), 3878-3895.
10.1002/2016WR019933Schleder, G., Padilha, A., Acosta, C., Costa, M., and Fazzio, A., 2019, From DFT to machine learning: recent approaches to materials science-a review, J. Phys. Mater., 2(11), 032001.
10.1088/2515-7639/ab084bSenozetnik, M., Herga, Z., Subic, T., Bradesko, L., Kenda, K., Klemen, K., Pergar, P., and Mladenic, D., 2018, IoT middle-ware for water management, Proceedings, 2, 696.
10.3390/proceedings2110696Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D., 2016, Mastering the game of Go with deep neural networks and tree search, Nature, 529(28), 484-489.
10.1038/nature16961Stackelberg, P.E., Belitz, K., Brown, C.J., Erickson, M.L., Elliott, S.M., Kauffman, L.J., Ransom, K.M., and Reddy, J.E., 2021, Machine learning predictions of pH in the glacial aquifer system, northern USA, Groundwater, 59(3), 352-368.
10.1111/gwat.1306333314084PMC8246943Stigter, T.Y., Ribeiro, L., and Carvalho Dill, A.M.M., 2006, Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinization and nitrate contamination levels in two agricultural regions in the south of Portugal, Hydrogeology Journal, 14, 79-99.
10.1007/s10040-004-0396-3UK Research and Innovation (UKRI), 2019 Digital twins for the next generation of geoscience prediction and understanding: OneGeology 4.0., http://www.onegeology.org/docs/newsEvents/digital-Twin-leaflet.pdf.
Vu, M.T., Jardani, A., Massei, N., and Fournier, M., 2021, Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network, J. Hydrol., 597, 125776.
10.1016/j.jhydrol.2020.125776Wadekar, S., Vakare, V., Prajapati, R., Yadav, S., and Yadav, V., 2016, Smart water management using IOT, IEEE, doi: 10.1109/WECON.2016.7993425
10.1109/WECON.2016.7993425Yoon, H., Jun, S.-C., Hyun, Y., Bae, G.-O., and Lee, K.-K., 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.
10.1016/j.jhydrol.2010.11.002Zhang, J., Zhu, Y., Zhang, X., Ye, M., and Yang, J., 2018, Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas, J. Hydrol., 561, 918-929.
10.1016/j.jhydrol.2018.04.065- Publisher :The Korean Society of Soil and Groundwater Environment
- Publisher(Ko) :한국지하수토양환경학회
- Journal Title :Journal of Soil and Groundwater Environment
- Journal Title(Ko) :지하수토양환경
- Volume : 28
- No :1
- Pages :18-39
- Received Date : 2022-10-20
- Revised Date : 2022-11-03
- Accepted Date : 2022-11-17
- DOI :https://doi.org/10.7857/JSGE.2023.28.S.018


Journal of Soil and Groundwater Environment





