Imputation methods for recovering streamflow observation: A methodological review

References

• Adeloye, A. J., & Rustum, R. (2012). Self-organising map rainfall-runoff multivariate modelling for runoff reconstruction in inadequately gauged basins. Hydrology Research, 43(5), 603. https://doi.org/10.2166/nh.2012.017
   Web of Science ®Google Scholar
• Adeloye, A. J., Rustum, R., & Kariyama, I. D. (2011). Kohonen self-organizing map estimator for the reference crop evapotranspiration. Water Resources Research, 47(8), 1–19. https://doi.org/10.1029/2011WR010690
   PubMedGoogle Scholar
• Ahmat Zainuri, N., Jemain, A. A., & Muda, N. (2015). A comparison of various imputation methods for missing values in air quality data. Sains Malaysiana, 44(3), 449–21. https://doi.org/10.17576/jsm-2015-4403-17
   Web of Science ®Google Scholar
• Aljuaid, T., & Sasi, S. (2017). Proper imputation techniques for missing values in data sets. 2016 International Conference on Data Science and Engineering (ICDSE). https://doi.org/10.1109/ICDSE.2016.7823957.
   Google Scholar
• Allawi, M. F., Jaafar, O., Hamzah, F. M., Abdullah, S. M. S., & El-Shafie, A. (2018). Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Environmental Science and Pollution Research, 25(14), 13446–13469. https://doi.org/10.1007/s11356-018-1867-8
   PubMed Web of Science ®Google Scholar
• Allawi, M. F., Jaafar, O., Mohamad Hamzah, F., Mohd, N. S., Deo, R. C., & El-Shafie, A. (2017). Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: A case study for a semi-arid region. Theoretical and Applied Climatology, 134(1–2), 545–563.. https://doi.org/10.1007/s00704-017-2292-5
   PubMed Web of Science ®Google Scholar
• Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., Van Griensven, A., Van Liew, M. W., Kannan, N., & Jha, M. K. (2012). SWAT: Model use, calibration, and validation. American Society of Agricultural and Biological Engineers, 55(4), 1491–1508. https://doi.org/10.13031/2013.42256
   Web of Science ®Google Scholar
• Bagus, I., & Narinda, G. (2016). Missing value imputation using KNN method optimized with memetic algorithm. E-Proceeding Engineering (pp. 1098–1105).
   Google Scholar
• Beauchamp, J. J., Downing, D. J., & Railsback, S. F. (1989). Comparison of regression and time-series methods for synthesizing missing streamflow records. Water Resources Bulletin, 25(5), 961–975. https://doi.org/10.1111/jawr.1989.25.issue-5
   Google Scholar
• Ben Aissia, M. A., Chebana, F., & Ouarda, T. B. M. J. (2017). Multivariate missing data in hydrology – Review and applications. Advances in Water Resources, 110(2017), 299–309. https://doi.org/10.1016/j.advwatres.2017.10.002
   Web of Science ®Google Scholar
• Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464–469. https://doi.org/10.1111/j.1467-842X.2001.tb00294.x
   PubMed Web of Science ®Google Scholar
• Bertsimas, D., Pawlowski, C., & Zhuo, Y. D. (2018). From predictive methods to missing data imputation: An optimization approach. Journal of Machine Learning Research, 18(1), 1–39. http://jmlr.org/papers/v18/17-073.html
   Google Scholar
• Blend, D., & Marwala, T. (2008). Comparison of data imputation techniques and their impact. Scientific Commons, 7. Retrieved from https://www.researchgate.net/publication/23625477_Comparison_of_Data_Imputation_Techniques_and_their_Impact/citations
   Google Scholar
• Carpenter, D. H. (1985). Cost-effectiveness of the federal stream-gauging program in Virginia. Towson.
   Google Scholar
• Caruso, C., & Quarta, F. (1998). Interpolation methods comparison. Computers & Mathematics with Applications, 35(12), 109–126. https://doi.org/10.1016/S0898-1221(98)00101-1
   Web of Science ®Google Scholar
• Chen, F. W., & Liu, C. W. (2012). Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy and Water Environment, 10(3), 209–222. https://doi.org/10.1007/s10333-012-0319-1
   Web of Science ®Google Scholar
• Chen, J., & Shao, J. (2001). Jackknife variance estimation for nearest-neighbor imputation. Journal of the American Statistical Association, 96(453), 260–269. https://doi.org/10.1198/016214501750332839
   Web of Science ®Google Scholar
• Chen, S. H., & Pollino, C. A. (2012). Good practice in Bayesian network modelling. Environmental Modelling & Software, 37(2012), 134–145. https://doi.org/10.1016/j.envsoft.2012.03.012
   Web of Science ®Google Scholar
• Čisar, P., & Čisar, S. M. (2011). Optimization methods of EWMA statistics. Acta Polytechnica Hungarica, 8(5), 73–87. Retrieved from https://www.semanticscholar.org/paper/Optimization-Methods-of-EWMA-Statistics-%C4%8Cisar-Cisar/1f98b8a64d18489b513716c9277a146dac7e915a
   Web of Science ®Google Scholar
• Collins, A. J., Fox, J., & Long, J. S. (1991). Modern methods of data analysis. The Statistician, 40(4), 458. https://doi.org/10.2307/2348744
   Google Scholar
• Cooper, G. E., Herskovits, E., & Edu, F. (1992). A Bayesian method for the induction of probabilistic networks from data. Journal of Machine Learning, 9(4), 309–347. https://doi.org/10.1007/BF00994110
   Google Scholar
• Croninger, R. G., & Douglas, K. M. (2005). Missing data and institutional research, New Dir. New Directions for Institutional Research, 2005(127), 33–49. https://doi.org/10.1002/ir.154
   Google Scholar
• De Silva, R. P., Dayawansa, N. D. K., & Ratnasiri, M. D. (2007). A comparison of methods used in estimating missing rainfall data. Journal of Agricultural Science, 3(2), 101. https://doi.org/10.4038/jas.v3i2.8107
   Google Scholar
• Dong, Y., & Peng, C.-Y. J. (2013). Principled missing data method for researchers. Springer Plus, 2(1), 222. https://doi.org/10.1186/2193-1801-2-222
   PubMed Web of Science ®Google Scholar
• Eash, D. A., Barnes, K. K., & Veilleux, A. G. (2013). Methods for estimating annual exceedance-probability discharges for streams in iowa, based on data through water year 2010. U.S. Geological Survey Scientific Investigations Report 2013–5086, 63 p. with appendix.
   Google Scholar
• Elshorbagy, A., Simonovic, S. P., & Panu, U. S. (2002). Estimation of missing streamflowdata using principles of chaos theory. Journal of Hydrology, 255(1–4), 123–133. https://doi.org/10.1016/S0022-1694(01)00513-3
   Web of Science ®Google Scholar
• Erdal, H. I., & Karakurt, O. (2013). Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. Journal of Hydrology, 477(2013), 119–128. https://doi.org/10.1016/j.jhydrol.2012.11.015
   Web of Science ®Google Scholar
• Fleig, A. K., Tallaksen, L. M., Hisdal, H., & Hannah, D. M. (2011). Regional hydrological drought in north-western Europe: Linking a new regional drought area index with weather types. Hydrological Processes, 25(7), 1163–1179. https://doi.org/10.1002/hyp.7644
   Web of Science ®Google Scholar
• Fontaine, R. A. (1982). Cost effective stream-gaging strategies for Maine. Augusta.
   Google Scholar
• Gao, Y. (2017). Dealing with missing data in hydrology - Data analysis of discharge and groundwater time-series in Northeast Germany. Freie Universität Berlin.
   Google Scholar
• Gao, Y., Merz, C., Lischeid, G., & Schneider, M. (2018). A review on missing hydrological data processing. Environmental Earth Sciences, 77(2), 47. https://doi.org/10.1007/s12665-018-7228-6
   Web of Science ®Google Scholar
• Gassman, P. W., Arnold, J. G., Srinivasan, R., & Reyes, M. (2010). The worldwide use of the SWAT model: Technological drivers, networking impacts, and simulation trends. 21st century watershed technology: Improving water quality and environment transacation ASABE (pp. 21–24). https://doi.org/10.1088/0953-8984/6/4/007.
   Google Scholar
• Gassman, P. W., Sadeghi, A. M., & Srinivasan, R. (2014). Applications of the SWAT model special section: Overview and insights. Journal of Environmental Quality, 43(1), 1. https://doi.org/10.2134/jeq2013.11.0466
   PubMed Web of Science ®Google Scholar
• Gill, M. K., Asefa, T., Kaheil, Y., & McKee, M. (2007). Effect of missing data on performance of learning algorithms for hydrologic predictions: Implications to an imputation technique. Water Resources Research, 43(7), 1–12. https://doi.org/10.1029/2006WR005298
   PubMed Web of Science ®Google Scholar
• Goa, Y. (2017). Dealing with missing data in hydrology - Data analysis of discharge and groundwater time-series in Northeast Germany. Freie Universität Berlin.
   Google Scholar
• Gómez-Carracedo, M. P., Andrade, J. M., López-Mahía, P., Muniategui, S., & Prada, D. (2014). A practical comparison of single and multiple imputation methods to handle complex missing data in air quality datasets. Chemometrics and Intelligent Laboratory Systems, 134(2014), 23–33. https://doi.org/10.1016/j.chemolab.2014.02.007
   Web of Science ®Google Scholar
• Graham, J. W. (2009). Missing data analysis : Making it work in the real world. Annual Review of Psychology, 60(1), 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530
   PubMed Web of Science ®Google Scholar
• Gupta, P., & Srinivasan, R. (2011). Missing data prediction and forecasting for water quantity data. International Conference on Modeling, Simulation and Control IPCSIT Vol.10. 10 (pp. 98–102). https://doi.org/10.7763/IPCSIT.
   Google Scholar
• Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-381479-1.00001-0
   Google Scholar
• Harvey, C. L., Dixon, H., & Hannaford, J. (2012). An appraisal of the performance of data-infilling methods for application to daily mean river flow records in the UK. Hydrology Research, 43(5), 618–637. https://doi.org/10.2166/nh.2012.110
   Web of Science ®Google Scholar
• Hasan, H., Ahmad, S., Osman, B. M., Sapri, S., & Othman, N. (2017). A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study. Proceedings of the 24th national symposium mathematical sciences American institute of physics. https://doi.org/10.1063/1.4995930.
   Google Scholar
• Hipel, K. W. (1995). Stochastic and statistical methods in hydrology and environmental engineering. Stochastic Hydrology and Hydraulics, 9(1), 1–11. https://doi.org/10.1007/BF01581755
   Google Scholar
• Honaker, J., & King, G. (2010). What to do about missing values in time-series cross-section data. American Journal of Political Science, 54(2), 561–581. https://doi.org/10.1111/j.1540-5907.2010.00447.x
   Web of Science ®Google Scholar
• Hsu, K., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10), 2517–2530. https://doi.org/10.1029/95WR01955
   Web of Science ®Google Scholar
• Huo, J. (2005). Application of statistical methods and process models for the design and analysis of activated sludge Wastewater Treatment Plants (WWTPs). University of Tennessee.
   Google Scholar
• Huo, J., Cox, C. D., Seaver, W. L., Robinson, R. B., & Jiang, Y. (2010). Application of two-directional time series models to replace missing data. Journal of Environmental Engineering, 136(4), 435–443. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000171
   Web of Science ®Google Scholar
• Hutchinson, M. F., & Gessler, P. E. (1994). Splines - more than just a smooth interpolator. Geoderma, 62(1–3), 45–67. https://doi.org/10.1016/0016-7061(94)90027-2
   Web of Science ®Google Scholar
• Ingsrisawang, L., & Potawee, D. (2012). Multiple imputation for missing data in repeated measurements using MCMC and copulas. International MultiConference of engineering and computer science II (pp. 1–5).
   Google Scholar
• Jain, A., & Indurthy, S. K. V. P. (2003). Comparative analysis of event-based rainfall-runoff modeling techniques—Deterministic, statistical, and artificial neural networks. Water, 8(2), 93–98. https://doi.org/10.1061/(ASCE)1084
   Google Scholar
• Jeong, D. I., & Kim, Y. O. (2009). Combining single-value streamflow forecasts - A review and guidelines for selecting techniques. Journal of Hydrology, 377(3–4), 284–299. https://doi.org/10.1016/j.jhydrol.2009.08.028
   Web of Science ®Google Scholar
• John, G. H., & Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. Proceedings of the eleventh conference on uncertainty in artificial intelligence (pp. 338–345). Morgan Kaufmann. Retrieved October 26, 2018, from http://web.cs.iastate.edu/~honavar/bayes-continuous.pdf
   Google Scholar
• Johnston, C. A. (1999). Development and evaluation of infilling methods for missing hydrologic and chemical watershed monitoring data. Virginia Polytechnic Institute.
   Google Scholar
• Kabir, G., Tesfamariam, S., Hemsing, J., & Sadiq, R. (2019). Handling incomplete and missing data in water network database using imputation methods. Sustainable and Resilient Infrastructure, 00, 1–13. https://doi.org/10.1080/23789689.2019.1600960
   Google Scholar
• Kalteh, A. M., Hjorth, P., & Berndtsson, R. (2007). Review of the Self-Organizing Map (SOM) approach in water resources: Analysis, modelling and application. Environmental Modelling & Software, 23(7), 835–845. https://doi.org/10.1016/j.envsoft.2009.01.008
   Web of Science ®Google Scholar
• Kalton, G., & Kish, L. (1984). Some efficient random imputation methods. Communications in Statistics - Theory Methods., 13(16), 1919–1939. https://doi.org/10.1080/03610928408828805
   Web of Science ®Google Scholar
• Kamaruzaman, I. F., Wan Zin, W. Z., & Mohd Ariff, N. (2017). A comparison of method for treating missing daily rainfall data in Peninsular Malaysia. Malaysian Journal of Fundamental & Applied Sciences, 13(4–1), 375–380. https://doi.org/10.11113/mjfas.v13n4–1.781
   Google Scholar
• Karakurt, O., Erdal, H. I., Namli, E., Yumurtaci-Aydogmus, H., & Turkkan, Y. S. (2013). Comparing ensembles of decision trees and Neural networks for one-day-ahead stream flow predict. Science Park, 1(17), 1–12. https://doi.org/10.9780/23218045/1172013/41
   Google Scholar
• Kim, J., & Pachepsky, Y. A. (2010). Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. Journal of Hydrology, 394(3–4), 305–314. https://doi.org/10.1016/j.jhydrol.2010.09.005
   Web of Science ®Google Scholar
• Kim, M., Baek, S., Ligaray, M., Pyo, J., Park, M., & Cho, K. H. (2015). Comparative studies of different imputation methods for recovering streamflow observation. Water (Switzerland), 7(12), 6847–6860. https://doi.org/10.3390/w7126663
   Google Scholar
• Kim, S. U., & Lee, K. S. (2009). Regional low flow frequency analysis using Bayesian regression and prediction at ungauged catchment in Korea. KSCE Journal of Civil Engineering, 14(1), 87–98. https://doi.org/10.1007/s12205-010-0087-7
   Web of Science ®Google Scholar
• Kohonen, T., Oja, E., Simula, O., Visa, A., & Kangas, J. (1996). Engineering applications of the self-organizing map. Proceedings of the IEEE, 84(10), 1358–1383. https://doi.org/10.1109/5.537105
   Web of Science ®Google Scholar
• Krysanova, V., & White, M. (2015). Advances in water resources assessment with SWAT—an overview. Hydrological Sciences Journal, 60(5), 1–13. https://doi.org/10.1080/02626667.2015.1029482
   Web of Science ®Google Scholar
• Kumar, A. S., Kumar, A., Krishnan, R., Chakravarthi, B., & Deekshatalu, B. L. (2017). Soft computing in remote sensing applications. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 87(4): 503–517.  https://doi.org/10.1007/s40010-017-0431-0
   Google Scholar
• Lee, H., & Kang, K. (2015). Interpolation of missing precipitation data using kernel estimations for hydrologic modeling. Advances in Meteorology, 2015(5), 12. https://doi.org/10.1155/2015/935868
   Web of Science ®Google Scholar
• Little, R., & An, H. (2004). Robust likelihood-based analysis of multivariate Data with missing values. Statistica Sinica, 14(3), 949–968. U.S. Geological Survey Scientific Investigations Report 2013–5086, 63 p. with appendix. Retrieved from https://www.jstor.org/stable/24307424?seq=1
   Google Scholar
• Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). A JOHN WILEY & SONS, INC. https://doi.org/10.1002/9781119013563
   Google Scholar
• Machiwal, D., & Jha, M. K. (2008). Comparative evaluation of statistical tests for time series analysis : Application to hydrological time series. Hydrological Sciences Journal, 53(2), 353–366. https://doi.org/10.1623/hysj.53.2.353
   Web of Science ®Google Scholar
• Marsh, H. W. (1998). Pairwise deletion for missing data in structural equation models: Nonpositivedefinite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling A Multidisciplinary Journal, 5(1), 22–36. https://doi.org/10.1080/10705519809540087
   Web of Science ®Google Scholar
• McDonald, R. A., Thurston, P. W., & Nelson, M. R. (2000). A Monte Carlo study of missing item methods. Organisational Research Methods, 3(1), 71–92. https://doi.org/10.1177/109442810031003
   Web of Science ®Google Scholar
• McKnight, P. E. (2007). Missing data : A gentle introduction. Guilford Press.
   Google Scholar
• Meadows, E. A., & Jeffcoat, H. H. (1990). Water resources publication for Alabama, 1857-1990. Denver: U.S. Geological Survey Books and Open-File Reports Federal Center.
   Google Scholar
• Minns, A. W., & Hall, M. J. (1996). Artificial neural networks as rainfall- runoff models. Hydrological Sciences Journal, 41(3), 399–417. https://doi.org/10.1080/02626669609491511
   Web of Science ®Google Scholar
• Miró, J. J., Caselles, V., & Estrela, M. J. (2017). Multiple imputation of rainfall missing data in the Iberian Mediterranean context. Atmospheric Research, 197(2017), 313–330. https://doi.org/10.1016/j.atmosres.2017.07.016
   Web of Science ®Google Scholar
• Mispan, M. R., Rahman, N. F. A., Ali, M. F., Khalid, K., Bakar, M. H. A., & Haron, S. H. (2015). Missing river discharge data imputation Approach using artificial neural network. Journal of Agricultural and Biological Science, 10(22), 10480–10485. Retrieved from http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1215_3088.pdf
   Google Scholar
• Moahmed, T. A., El Gayar, N., & Atiya, A. F. (2014). Forward and backward forecasting ensembles for the estimation of time series missing data. IAPR workshop on artificial neural networks in pattern recognition (pp. 93–104). https://doi.org/10.1007/978-3-642-12159-3.
   Google Scholar
• Moritz, S., & Bartz-Beielstein, T. (2017). imputeTS: Time series missing value imputation in R. The R Journal, 9(1), 207–218. https://doi.org/10.32614/RJ-2017-009
   Google Scholar
• Mwale, F. D., Adeloye, A. J., & Rustum, R. (2012). Infilling of missing rainfall and streamflow data in the Shire River basin, Malawi - A self organizing map approach. Physics and Chemistry of the Earth, 50–52(2012), 34–43. https://doi.org/10.1016/j.pce.2012.09.006
   Web of Science ®Google Scholar
• Neitsch, S., Arnold, J., Kiniry, J., & Williams, J. (2011). SWAT theoretical documentation version 2009. Texas Water Resources Institute. Texas: Texas A&M University System. https://doi.org/10.1016/j.scitotenv.2015.11.063
   Google Scholar
• Nishanth, K. J., & Ravi, V. (2013). A computational intelligence based online data imputation method: An application for banking. Journal of Information Processing Systems, 9(4), 633–650. https://doi.org/10.3745/JIPS.2013.9.4.633
   Google Scholar
• Norliyana, W., Ismail, W., Zawiah, W., Zin, W., & Ibrahim, W. (2017). Estimation of rainfall and stream flow missing data for Terengganu, Malaysia by using interpolation technique methods. Malaysian Journal of Fundamental & Applied Sciences, 13(3), 213–217. https://doi.org/10.11113/mjfas.v13n3.578
   Web of Science ®Google Scholar
• Oosthuizen, N., Hughes, D. A., Kapangaziwiri, E., Mwenge Kahinda, J.-M., & Mvandaba, V. (2018). Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin. Proceedings of the international association of hydrological sciences, copernicus publications on behalf of the international association of hydrological sciences (pp. 11–16). https://doi.org/10.5194/piahs-378-11-2018.
   Google Scholar
• Peña-angulo, D., Nadal-romero, E., González-hidalgo, J. C., Albaladejo, J., Andreu, V., Bagarello, V., Barhi, H., Batalla, R. J., Bernal, S., Bienes, R., Campo, J., Campo-bescós, M. A., Canatario-duarte, A., Cantón, Y., Casali, J., Castillo, V., Cerdà, A., Cheggour, A., Cid, P., Cortesi, N., … Zorn, M. (2019). Spatial variability of the relationships of runo ff and sediment yield with weather types throughout the Mediterranean basin. Journal of Hydrology, 571(2019), 390–405. https://doi.org/10.1016/j.jhydrol.2019.01.059
   Web of Science ®Google Scholar
• Perry, M. B. (2011). The exponentially weighted moving average Wiley encyclopedia of operations … Management Science (pp. 1–9). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470400531.eorms0314.
   Google Scholar
• Pigott, T. D. (2001). A review of methods for missing data. Educational Research and Evaluation, 7(4), 353–383. https://doi.org/10.1076/edre.7.4.353.8937
   Google Scholar
• Plaia, A., & Bondì, A. L. (2006). Single imputation method of missing values in environmental pollution data sets. Atmospheric Environment, 40(38), 7316–7330. https://doi.org/10.1016/j.atmosenv.2006.06.040
   Web of Science ®Google Scholar
• Quinlan, J. R., & Kaufmann, M. (1994). C4.5: Programs for machine learning. Khawer Academic.
   Google Scholar
• Raaijmakers, Q. A. W. (1999). Effectiveness of different missing data treatments in surveys with likert-type data: Introducing the relative mean substitution approach. Educational and Psychological Measurement, 59(5), 725–748. https://doi.org/10.1177/0013164499595001
   Google Scholar
• Rahman, N. A., Deni, S. M., & Ramli, N. M. (2017). Generalized linear model for estimation of missing daily rainfall data. 4th international conference on applied physics (pp. 0800191–0800198). https://doi.org/10.1063/1.4981003.
   Google Scholar
• Rahman, N. F. A., Ali, M. F., Mohd, M. S. F., Khalid, K., Haron, S. H., Kamaruddin, H., & Mispan, M. R. (2015). Semi distributed hydro climate model; The Xls2NCascii program approach for weather generator. Journal of Agricultural and Biological Science, 10(15), 6619–6622. Retrieved from https://www.researchgate.net/publication/281264842_Semi_distributed_hydro_climate_model_The_Xls2NCascii_program_approach_for_weather_generator
   Google Scholar
• Rajagopalan, B., & Lall, U. (1999). Ak-nearest-neighbor simulator for daily precipitation and other weather variables. Water Resources Research, 35(10), 3089–3101. https://doi.org/10.1029/1999WR900028
   Web of Science ®Google Scholar
• Refsgaard, J. C., Storm, B., & Clausen, T. (2010). Système Hydrologique Europeén (SHE): Review and perspectives after 30 years development in distributed physically-based hydrological modelling. Hydrology Research, 41(5), 355. https://doi.org/10.2166/nh.2010.009
   Web of Science ®Google Scholar
• Regonda, S. K., Seo, D. J., Lawrence, B., Brown, J. D., & Demargne, J. (2013). Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach. Journal of Hydrology, 497(2013), 80–96. https://doi.org/10.1016/j.jhydrol.2013.05.028
   Web of Science ®Google Scholar
• Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–250. https://doi.org/10.1080/00401706.1959.10489860
   Google Scholar
• Roth, P. L., Switzer, F. S., III, & Switzer, D. M. (1999). Missing data in multiple item scales: A Monte Carlo analysis of missing data techniques. Organisational Research Methods, 2(3), 211–232. https://doi.org/10.1177/109442819923001
   Web of Science ®Google Scholar
• Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592. https://doi.org/10.1093/biomet/63.3.581
   Web of Science ®Google Scholar
• Sakke, N., Ithnin, H., Ibrahim, M. H., Pah, T., & Syed, R. (2016). Hydrological drought and the sustainability of water resources in Malaysia : An analysis of the properties of the Langat Basin, Selangor. Malaysian Journal of Society and Space, 12(7), 133–146. Retrieved from https://www.researchgate.net/publication/313497530_Kemarau_hidrologi_dan_kelestarian_sumber_air_di_Malaysia_Kajian_analisis_sifat_Lembangan_Langat_Selangor
   Google Scholar
• Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210–229. https://doi.org/10.1147/rd.33.0210
   Web of Science ®Google Scholar
• Santosa, B., Legono, D., & Suharyanto. (2014). Prediction of missing streamflow data using principle of information entropy. Civil Engineering Dimension, 16(1), 40–45. https://doi.org/10.9744/ced.16.1.40-45
   Google Scholar
• Schafer, J. L. J. (1997). Analysis of incomplete multivariate data. Chapman & Hall.
   Google Scholar
• Schenker, N., & Taylor, J. M. G. (1996). Partially parametric techniques for multiple imputation. Computational Statistics & Data Analysis, 22(4), 425–446. https://doi.org/10.1016/0167-9473(95)00057-7
   Web of Science ®Google Scholar
• Schneider, T. (2001). Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal of Climate, 14(5), 853–871. https://doi.org/10.1175/1520-0442(2001)014<0853:AOICDE>2.0.CO;2
   Web of Science ®Google Scholar
• Shields, F. D., & Sanders, T. G. (1986). Water quality effects of excavation and diversion. Journal of Environmental Engineering, 112(2), 211–228. https://doi.org/10.1061/(ASCE)0733-9372(1986)112:2(211)
   Web of Science ®Google Scholar
• Smith, J., & Eli, R. N. (1995). Neural-network models of rainfall-runoff process. Journal of Water Resources Planning and Management, 121(6), 499–508. https://doi.org/10.1061/(ASCE)0733-9496(1995)121:6(499)
   Web of Science ®Google Scholar
• Somwanshi, P. D., & Chaware, S. M. (2014). A review on: Advanced Artificial Neural Networks (ANN) approach for IDS by layered method. International Journal of Computer Science and Information Technologies, 5(4), 5129–5131. Retrieved from http://ijcsit.com/docs/Volume%205/vol5issue04/ijcsit2014050466.pdf
   Google Scholar
• Spiring, F. (2007). Introduction to statistical quality control. Technometrics, 49(1), 108–109. https://doi.org/10.1198/tech.2007.s465
   Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics (6th ed.). Pearson Education Limited.
   Google Scholar
• Teegavarapu, R. S. V., & Chandramouli, V. (2005). Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. Journal of Hydrology, 312(1–4), 191–206. https://doi.org/10.1016/j.jhydrol.2005.02.015
   Web of Science ®Google Scholar
• Tencaliec, P. (2017). Developments in statistics applied to hydrometeorology: Imputation of streamflow data and semiparametric precipitation modeling. Universite Grenoble Alpes.
   Google Scholar
• Tencaliec, P., Favre, A., Prieur, C., & Mathevet, T. (2015). Reconstruction of missing daily streamflow data using dynamic regression models. Water Resources Research: American Geophysical Union, 51(12), 9447–9463. https://doi.org/10.1002/2015WR017399
   Web of Science ®Google Scholar
• Tsintikidis, D., Haferman, J. L., Anagnostou, E. N., Krajewski, W. F., & Smith, T. F. (1997). A neural network approach to estimating rainfall from spaceborne microwave data. IEEE Transactions on Geoscience and Remote Sensing,35(5), 1079–1093. http://doi.org/10.1109/36.628775
   Google Scholar
• Tuppad, P., Mankin, K. R. D., Lee, T., Srinivasan, R., & Arnold, J. G. (2011). Soil and Water Assessment Tool (SWAT) hydrologic/water quality model: Extended capability and wider adoption. American Society of Agricultural and Biological Engineers, 54(5), 1677–1684. https://doi.org/10.13031/2013.39856
   Web of Science ®Google Scholar
• Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3–4), 312–318. https://doi.org/10.1016/j.ecolmodel.2006.11.033
   Web of Science ®Google Scholar
• Varga, M., Balogh, S., & Csukas, B. (2016). GIS based generation of dynamic hydrological and land patch simulation models for rural watershed areas. Information Processing in Agriculture, 3(1), 1–16. https://doi.org/10.1016/j.inpa.2015.11.001
   Google Scholar
• Vigerstol, K. L., & Aukema, J. E. (2011). A comparison of tools for modeling freshwater ecosystem services. Journal of Environmental Management, 92(10), 2403–2409. https://doi.org/10.1016/j.jenvman.2011.06.040
   PubMed Web of Science ®Google Scholar
• Wallis, J. R., Lettenmaier, D. P., & Wood, E. F. (1991). A daily hydroclimatological data set for the continental United States. Water Resources Research, 27(7), 1657–1663. https://doi.org/10.1029/91WR00977
   Web of Science ®Google Scholar
• Widaman, K. F. (2006). Missing data: What to do with or without them. Monographs of the Society for Research in Child Development, 71(1), 210–211. https://doi.org/10.1111/j.1540-5834.2006.00404.x
   Web of Science ®Google Scholar
• Woodall, W. H., & Mahmoud, M. A. (2005). The inertial properties of quality control charts. Technometrics, 47(4), 425–436. https://doi.org/10.1198/004017005000000256
   Web of Science ®Google Scholar
• Worland, S. C., Farmer, W. H., & Kiang, J. E. (2018). Improving predictions of hydrological low-flow indices in ungaged basins using machine learning. Environmental Modelling & Software, 101(2018), 169–182. https://doi.org/10.1016/j.envsoft.2017.12.021
   Web of Science ®Google Scholar
• Yakowitz, S., & Karlsson, M. (1987). Nearest neighbor methods for time series, with application to rainfall/runoff prediction. Advances in the statistical sciences: Stochastic hydrology (pp. 149–160). Springer Netherlands. https://doi.org/10.1007/978-94-009-4792-4_9.
   Google Scholar
• Yashchin, E. (1987). Some aspects of the theory of statistical control schemes. IBM Journal of Research and Development, 31(2), 199–205. https://doi.org/10.1147/rd.312.0199
   Web of Science ®Google Scholar
• Yashchin, E. (1993). Statistical control schemes: Methods, applications and generalizations. International Statistical Review, 61(1), 41–66. https://doi.org/10.2307/1403593
   Web of Science ®Google Scholar
• Zeiger, S., & Hubbart, J. (2018). Assessing the difference between Soil and Water Assessment Tool (SWAT) simulated pre-development and observed developed loading regimes. Hydrology, 5(2), 29. https://doi.org/10.3390/hydrology5020029
   Web of Science ®Google Scholar
• Žliobaite, I., Hollmén, J., & Junninen, H. (2014). Regression models tolerant to massively missing data: A case study in solar-radiation nowcasting. Atmospheric Measurement Techniques, 7(12), 4387–4399. https://doi.org/10.5194/amt-7-4387-2014
   Web of Science ®Google Scholar