96
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Cessation time approach incorporating parametric and non-parametric machine-learning algorithms for recovery test data

&
Pages 1578-1590 | Received 19 Aug 2022, Accepted 30 May 2023, Published online: 13 Jul 2023

References

  • Agarwal, R.G., 1980. A new method to account for producing time effects when drawdown type curves are used to analyze pressure buildup and other test data. In: SPE annual technical conference and exhibition, 20. doi:10.2118/9289-MS.
  • Apaydin, E., 2020. Introduction to machine learning. 4th ed. Cambridge, MA: MIT Press.
  • Ashjari, J., 2013. Determination of storage coefficients during pumping and recovery. Groundwater, 51 (1), 122–127. doi:10.1111/j.1745-6584.2012.00917.x.
  • Ballukraya, P.N. and Sharma, K.K., 1991. Estimation of storativity from recovery data. Groundwater, 29 (4), 495–498. doi:10.1111/j.1745-6584.1991.tb00540.x.
  • Batu, V., 1998. Aquifer hydraulics: a comprehensive guide to hydrogeologic data analysis. New York, USA: Wiley.
  • Butler, J.J., 1988. Pumping tests in nonuniform aquifers: the radially symmetric case. Journal of Hydrology, 101, 15–30. doi:10.1016/0022-1694(88)90025-X
  • Chenaf, D. and Chapuis, R.P., 2002. Methods to determine storativity of infinite confined aquifers from a recovery test. Groundwater, 40 (4), 385–389. doi:10.1111/j.1745-6584.2002.tb02517.x.
  • Cheng, L., et al., 2019. An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data. Nature Communications, 10, 1798. doi:10.1038/s41467-019-09785-8.
  • Ciftci, E., 2019. A new approach for analyzing drawdown data from a partially penetrating well. Water Resources Management, 33, 2727–2739. doi:10.1007/s11269-019-02246-3
  • Çiftci, E. and Sahin, A.U., 2023. Regression-based interpretation method for confined aquifer pumping tests. Journal of Hydrologic Engineering, 28 (2), 04022041. doi:10.1061/(ASCE)HE.1943-5584.0002235.
  • Çimen, M., 2015. A straight-line method for analyzing residual drawdowns at an observation well. Mathematical Problems in Engineering, 2015 (1), 1–5. doi:10.1155/2015/978040.
  • Cooper, H.H. and Jacob, C.E., 1946. A generalized graphical method for evaluating formation constants and summarizing well-field history. Eos, Transactions American Geophysical Union, 27 (4), 526–534. doi:10.1029/TR027i004p00526.
  • Dashti, Z., et al., 2023. A literature review on pumping test analysis (2000–2022). Environmental Science and Pollution Research, 30, 9184–9206. doi:10.1007/s11356-022-24440-4
  • Driscoll, F.G., 1986. Groundwater and wells. 2nd ed. St. Paul, MN: Johnson Division, 1089.
  • Garamhegyi, T. et al., 2020. Revision of archive recovery tests using analytical and numerical methods on thermal water wells in sandstone and fractured carbonate aquifers in the vicinity of Budapest, Hungary. Environmental Earth Science, 79, 129. doi:10.1007/s12665-020-8835-6.
  • Garin, T., et al., 2022. Improving hydrogeological understanding through well-test interpretation by diagnostic plot and modelling: a case study in an alluvial aquifer in France. Hydrogeology Journal, 30 (1), 283–302. doi:10.1007/s10040-021-02426-9.
  • Goode, D.J., 1997. Composite recovery type curves in normalized time from Theis exact solution. Ground Water, 35, 672–678. doi:10.1111/j.1745-6584.1997.tb00133.x
  • Gunawardhana, L.N., et al., 2021. Analytical and numerical analysis of constant-rate pumping test data considering aquifer boundary effect. Environmental Earth Sciences, 80, 543. doi:10.1007/s12665-021-09833-x
  • Gupta, S., et al., 2022. Comparing the performance of machine learning algorithms using estimated accuracy. Measurement: Sensors, 24 (2022), 100432. doi:10.1016/j.measen.2022.100432.
  • Gurarslan, G. and Karahan, H., 2015. Solving inverse problems of groundwater-pollution-source identification using a differential evolution algorithm. Hydrogeology Journal, 23 (6), 1109–1119. doi:10.1007/s10040-015-1256-z.
  • Halford, K., et al., 2012. Advanced methods for modeling water-levels and estimating drawdowns with SeriesSEE, an Excel add-in. (Version 1.0: originally posted December 2012; Version 1.1: july 2016). Reston, VA: Techniques and Methods. doi:10.3133/tm4F4.
  • Hantush, M.S., 1961. Drawdown around a partially penetrating well: proceedings of the American Society of civil engineers. Journal of the Hydraulics Division, 87, 83–98. doi:10.1061/JYCEAJ.0000633
  • James, G., et al., 2015. An introduction to statistical learning. New York: Springer, 315.
  • Jekabsons, G., 2020. Toolboxes for Matlab/Octave. Available from: http://www.cs.rtu.lv/jekabsons/regression.html [Accessed April 2020].
  • Kawecki, M.W., 1993. Recovery analysis from pumping tests with stepped discharge. Ground Water, 31 (4), 585–592. doi:10.1111/j.1745-6584.1993.tb00591.x.
  • Kruseman, G.P. and de Ridder, N.A., 1990. Analysis and evaluation of pumping test data. 2nd ed. 47 vols. Wageningen, The Netherlands: International Institute for Land Reclamation and improvement.
  • Lal, A. and Datta, B., 2018. Development and implementation of support vector machine regression surrogate models for predicting groundwater pumping-induced saltwater intrusion into coastal aquifers. Water Resources Management, 32, 2405–2419. doi:10.1007/s11269-018-1936-2
  • Lee, S., Lee, K.K., and Yoon, H., 2019. Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors. Hydrogeology Journal, 27, 567–579. doi:10.1007/s10040-018-1866-3
  • Li, P., Qian, H., and Wu, J., 2014. Comparison of three methods of hydrogeological parameter determination in leaky aquifers using transient flow pumping tests. Hydrological Processes, 28 (4), 2293–2301. doi:10.1002/hyp.9803.
  • Mishra, G.C. and Chachadi, A.G., 1985. Analysis of flow to a large diameter well during the recovery period. Ground Water, 23 (5), 646–651. doi:10.1111/j.1745-6584.1985.tb01513.x.
  • Muneer, T. and Munawwar, S., 2006. Improved accuracy models for hourly diffuse solar radiation. Journal of Solar Energy Engineering, 128, 104–117. doi:10.1115/1.2148972
  • Naderi, M., 2019. Estimating confined aquifer parameters using a simple derivative-based method. Heliyon, 5 (10), e02657. doi:10.1016/j.heliyon.2019.e02657.
  • Neuman, S.P., 1975. Analysis of pumping test data from anisotropic unconfined aquifers considering delayed gravity response. Water Resources Research, 11 (2), 329–342. doi:10.1029/WR011i002p00329.
  • Pakzad, S.S., Roshan, N., and Ghalehnovi, M., 2023. Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber‑reinforced concrete. Scientific Reports, 13, 3646. doi:10.1038/s41598-023-30606-y
  • Sahin, A.U., 2016. A new inverse solution assessment for the recovery test using radial basis function collocation method. Water Resource Management, 30 (3), 947–962. doi:10.1007/s11269-015-1201-x.
  • Sahin, A.U., 2022. A novel optimisation framework for interpretation of unconfined aquifer pumping test data. In: Proceedings of the institution of civil engineers – water management. doi:10.1680/jwama.21.00115.
  • Sajehi-Hosseini, F., et al., 2018. A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Science of the Total Environment, 644, 954–962. doi:10.1016/j.scitotenv.2018.07.054
  • Samuel, M.P. and Jha, M.K., 2003. Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique. Journal of Irrigation and Drainage Engineering, 129 (5), 348–359. doi:10.1061/(ASCE)0733-9437(2003)129:5(348).
  • Schwartz, W.F. and Zhang, H., 2003. Fundamentals of groundwater. New York: John Wiley & Sons Inc.
  • Seyoum, W.M., Kwon, D., and Milewski, A.M., 2019. Downscaling GRACE TWSA data into high-resolution groundwater level anomaly using machine learning-based models in a glacial aquifer system. Remote Sensing, 11 (7), 824. doi:10.3390/rs11070824.
  • Shi, X., et al., 2022. Application of the Gaussian process regression method based on a combined kernel function in engine performance prediction. ACS Omega, 7 (45), 41732–41743. doi:10.1021/acsomega.2c05952.
  • Singh, S.K., 1999. Estimation of aquifer parameters from recovery data. In: V.P. Singh, I.W. Seo, and J.H. Sonu, eds. Proceedings of the international conference on water, environment, ecology, socioeconomics and health engineering; hydrologic modelling. Boca Raton: Water Resources Publications, LLC, 406–410.
  • Singh, S.K., 2003. Storage coefficient and transmissivity from residual drawdowns. Journal of Hydraulic Engineering, 129 (8), 637–644. doi:10.1061/(ASCE)0733-9429(2003)129:8(637).
  • Theis, C.V., 1935. The relation between the lowering of the Piezometric surface and the rate and duration of discharge of a well using ground-water storage. Eos, Transactions American Geophysical Union, 16 (2), 519–524. doi:10.1029/TR016i002p00519.
  • Todd, D.K., 1980. Groundwater hydrology. 2nd ed. New York: Wiley India Pvt. Ltd.
  • Trabucchi, M., Carrera, J., and Fernàndez‐Garcia, D., 2018. Generalizing Agarwal’s method for the interpretation of recovery tests under non‐ideal conditions. Water Resources Research, 54 (9), 6393–6407. doi:10.1029/2018WR022684.
  • Trinchero, P., et al., 2008. A new method for the interpretation of pumping tests in leaky aquifers. Groundwater, 46, 133–143. doi:10.1111/j.1745-6584.2007.00384.x
  • Uddin, S., et al., 2019. Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19, 281. doi:10.1186/s12911-019-1004-8
  • U.S. Department of the Interior (USDI), 1995. Ground water manual. U.S. Government Printing Office, Bureau of reclamation. 2nd ed. Washington, DC. https://www.usbr.gov/tsc/techreferences/mands/mands-pdfs/GndWater.pdf.
  • Willmann, M., et al., 2007. On the meaning of the transmissivity values obtained from recovery tests. Hydrogeology Journal, 15 (5), 833–842. doi:10.1007/s10040-006-0147-8.
  • Xu, T.F. and Valocchi, A.J., 2015. Data-driven methods to improve baseflow prediction of a regional groundwater model. Computers & Geosciences, 85, 124–136. doi:10.1016/j.cageo.2015.05.016
  • Zhang, L. and Chapuis, R.P., 2019. Recovery test after a constant-head test in a monitoring well: interpretation methods and new findings. Engineering Geology, 259, 105150. doi:10.1016/j.enggeo.2019.105150
  • Zheng, L., Guo, J.J., and Lei, Y., 2005. An improved straight-line fitting method for analyzing pumping test recovery data. Groundwater, 43 (6), 939–942. doi:10.1111/j.1745-6584.2005.00094.x.
  • Zhuang, C., et al., 2020. A type-curve method for the analysis of pumping tests with piecewise-linear pumping rates. Groundwater, 58 (5), 788–798. doi:10.1111/gwat.12981.
  • Zipper, S.C., et al., 2022. Quantifying streamflow depletion from groundwater pumping: a practical review of past and emerging approaches for water management. JAWRA Journal of the American Water Resources Association, 58, 289–312. doi:10.1111/1752-1688.12998

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.