References
- Adeloye, A.J., 1996. An opportunity loss model for estimating the value of streamflow data for reservoir planning. Water Resources Management, 10 (1), 45–79. doi:https://doi.org/10.1007/BF00698811.
- Ahmat Zainuri, N., Aziz Jemain, A., and Muda, N., 2015. A comparison of various imputation methods for missing values in air quality data. Sains Malaysiana, 44 (3), 449–456. doi:https://doi.org/10.17576/jsm-2015-4403-17.
- Ahn, K.H., 2021. Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas. Hydrology and Earth System Sciences, 25 (8), 4319–4333. doi:https://doi.org/10.5194/hess-25-4319-2021.
- Baddoo, T.D., et al., 2021. Comparison of missing data infilling mechanisms for recovering a real-world single station streamflow observation. International Journal of Environmental Research and Public Health, 18 (16), 8375. doi:https://doi.org/10.3390/ijerph18168375.
- 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. doi:https://doi.org/10.1111/j.1467-842X.2001.tb00294.x.
- Bertsimas, D., Pawlowski, C., and Zhuo, Y.D., 2018. From predictive methods to missing data imputation: an optimization approach. Journal of Machine Learning Research, 18 (2018), 1–39. Available from: http://jmlr.org/papers/v18/17-073.html
- Breiman, L., et al., 1984. Classification and regression trees. New York: Wadsworth Publishing.
- Campozano, L., et al., 2014. Evaluation of infilling methods for time series of daily precipitation and temperature: the case of the Ecuadorian Andes. Maskana, 5 (1), 99–115. doi:https://doi.org/10.18537/mskn.05.01.07.
- Carey, A.M. and Paige, G.B., 2016. Ecological site-scale hydrologic response in a semiarid rangeland watershed. Rangeland Ecology and Management, 69 (6), 481–490. doi:https://doi.org/10.1016/j.rama.2016.06.007.
- Chhabra, G., Vashisht, V., and Ranjan, J., 2017. A comparison of multiple imputation methods for data with missing values. Indian Journal of Science and Technology, 10 (19), 1–7. doi:https://doi.org/10.17485/ijst/2017/v10i19/110646.
- De’ath, G. and Fabricius, K.E., 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81 (11), 3178–3192. doi:https://doi.org/10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2.
- Devineni, N., et al., 2013. A tree-ring-based reconstruction of Delaware River basin streamflow using hierarchical Bayesian regression. Journal of Climate, 26 (12), 4357–4374. doi:https://doi.org/10.1175/JCLI-D-11-00675.1.
- Donders, A.R.T., et al., 2006. Review: a gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59 (10), 1087–1091. doi:https://doi.org/10.1016/j.jclinepi.2006.01.014.
- Dong, Y. and Peng, C.-Y.J., 2013. Principled missing data methods for researchers. SpringerPlus, 2 (1), 1–17. doi:https://doi.org/10.1186/2193-1801-2-222.
- Erdal, H.I. and Karakurt, O., 2013. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. Journal of Hydrology, 477 (2013), 119–128. doi:https://doi.org/10.1016/j.jhydrol.2012.11.015.
- Gao, Y., 2017. Dealing with missing data in hydrology - data analysis of discharge and groundwater time-series in Northeast Germany. Germany: Freie Universität Berlin.
- Gelman, A. and Speed, T.P., 1999. Corrigendum: characterizing a joint probability distribution by conditionals. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61 (2), 483. doi:https://doi.org/10.1111/1467-9868.00189.
- Gill, M.K., et al., 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. doi:https://doi.org/10.1029/2006WR005298.
- Gires, A., Tchiguirinskaia, I., and Schertzer, D., 2021. Infilling missing data of binary geophysical fields using scale invariant properties through an application to imperviousness in urban areas. Hydrological Sciences Journal, 66 (7), 1197–1210. doi:https://doi.org/10.1080/02626667.2021.1925121.
- Hamzah, F.B., et al., 2020. Imputation methods for recovering streamflow observation : a methodological review. Cogent Environmental Science, 6 (1), 21. doi:https://doi.org/10.1080/23311843.2020.1745133.
- Hamzah, F.B., et al., 2021. A comparison of multiple imputation methods for recovering missing data in hydrological studies. Civil Engineering Journal, 7 (9), 1608–1619. doi:https://doi.org/10.28991/cej-2021-03091747.
- Harvey, C.L., Dixon, H., and 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. doi:https://doi.org/10.2166/nh.2012.110.
- Heitjan, D.F. and Rubin, D.B., 1990. Inference from coarse data via multiple imputation with application to age heaping. Journal of the American Statistical Association, 85 (410), 304–314. doi:https://doi.org/10.1080/01621459.1990.10476202.
- Islam Khan, S. and Hoque, A.S.M.L., 2020. SICE: an improved missing data imputation technique background and related works. Journal of Big Data, 7 (1), 37. doi:https://doi.org/10.1186/s40537-020-00313-w.
- Jamil, J.M., 2012. Partial least squares structural equation modelling with incomplete data: an investigation of the impact of imputation methods. The University of Bradford.
- Johnston, C.A., 1999. Development and evaluation of infilling methods for missing hydrologic and chemical watershed monitoring data. Virginia Polytechnic Institute and State University.
- Juahir, H., et al., 2008. The use of chemometrics analysis as a cost-effective tool in sustainable utilisation of water resources in the Langat River catchment. American-Eurasian Journal of Agricultural & Environmental Sciences, 4 (1), 258–265.
- Juahir, H., et al., 2010. Hydrological trend analysis due to land use changes at langat river basin. EnvironmentAsia, 3 (SPECIAL ISSUE), 20–31. doi:https://doi.org/10.14456/ea.2010.61.
- Juahir, H., et al., 2011. Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques. Environmental Monitoring and Assessment, 173 (1–4), 625–641. doi:https://doi.org/10.1007/s10661-010-1411-x.
- Kamaruzaman, I.F., Wan Zin, W.Z., and Mohd Ariff, N., 2017. A comparison of method for treating missing daily rainfall data in Peninsular Malaysia. Malaysian Journal of Fundamental and Applied Sciences, 13 (4), 375–380. (Special Issue on Some Advances in Industrial and Applied Mathematics). doi:https://doi.org/10.11113/mjfas.v13n4-1.781.
- Karakurt, O., et al., 2013. Comparing ensembles of decision trees and neural networks for one-day-ahead stream flow predict. Scientific Research Journal, 1 (15), 43–54. doi:https://doi.org/10.9780/23218045/1172013/41.
- Kim, S.U. and 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. doi:https://doi.org/10.1007/s12205-010-0087-7.
- Little, R.J.A. and Rubin, D.B. 2002. Statistical analysis with missing data, hlm. 2nd Edisi. Hoboken, New Jersey: John Wiley & Sons, Inc. doi:https://doi.org/10.1002/9781119013563.
- Memarian, H., et al., 2012. Trend analysis of water discharge and sediment load during the past three decades of development in the Langat basin, Malaysia. Hydrological Sciences Journal, 57 (6), 1207–1222. doi:https://doi.org/10.1080/02626667.2012.695073.
- Mispan, M.R., et al., 2015. Missing river discharge data imputation approach using artificial neural network. ARPN Journal of Engineering and Applied Sciences, 10 (22), 10480–10485.
- Mohamad Hamzah, F., Mohd Yusoff, S.H., and Jaafar, O., 2019. L-moment-based frequency analysis of high-flow at the Sungai Langat, Kajang, Selangor, Malaysia. Sains Malaysiana, 48 (7), 1357–1366. L-Moment-Based. doi:https://doi.org/10.17576/jsm-2019-4807-05.
- Moritz, M.S. and Bartz-Beielstein, T., 2017. imputeTS: time series missing value imputation in R. The R Journal, 9 (1), 207–218. doi:https://doi.org/10.32614/RJ-2017-009.
- Müller, K.-R., et al. 1997. Predicting time series with support vector machines. In: W. Gerstner, et al. (eds) Artificial Neural Networks — ICANN’97. ICANN 1997. Lecture Notes in Computer Science. Berlin Heidelberg: Springer. doi:https://doi.org/10.1007/bfb0020283
- Mwale, F.D., Adeloye, A.J., and 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. doi:https://doi.org/10.1016/j.pce.2012.09.006.
- Noorazuan, M., et al. 2003. GIS application in evaluating land use-land cover change and its impact on Hydrological regime in Langat River Basin, Malaysia. In: Proceedings of the Conference MapAsia 2003, Malaysia, Kuala Lumpur. February.
- Nor, S.M.C.M., et al., 2020. A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia. Bulletin of Electrical Engineering and Informatics, 9 (2), 635–643. doi:https://doi.org/10.11591/eei.v9i2.2090.
- Norazizi, N.A.A. and Deni, S.M. 2019. Comparison of Artificial Neural Network (ANN) and other imputation methods in estimating missing rainfall data at Kuantan Station. Soft Computing in Data Science, 5th International Conference, SCDS 2019, hlm, Singapore. Iizuka, Japan: Springer, 298–308. doi:https://doi.org/10.1007/978-981-15-0399-3_24.
- Pham, H., 2019. A new criterion for model selection. Mathematics, 7 (12), 12. doi:https://doi.org/10.3390/MATH7121215.
- Plaia, A. and Bondì, A.L., 2006. Single imputation method of missing values in environmental pollution data sets. Atmospheric Environment, 40 (38), 7316–7330. doi:https://doi.org/10.1016/j.atmosenv.2006.06.040.
- Puah, Y.J., et al., 2016. River catchment rainfall series analysis using additive Holt – winters method. Journal of Earth System Science, 125 (2), 269–283. doi:https://doi.org/10.1007/s12040-016-0661-6.
- Rahman, N.F.A., et al., 2015. Semi distributed hydro climate model; The Xls2NCascii program approach for weather generator. ARPN Journal of Engineering and Applied Sciences, 10 (15), 6619–6622.
- Regonda, S.K., et al., 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. doi:https://doi.org/10.1016/j.jhydrol.2013.05.028.
- Schenker, N. and Taylor, J.M.G., 1996. Partially parametric techniques for multiple imputation. Computational Statistics and Data Analysis, 22 (4), 425–446. doi:https://doi.org/10.1016/0167-9473(95)00057-7.
- Schmitt, P., Mandel, J., and Guedj, M., 2015. A comparison of six methods for missing data imputation. Journal of Biometrics & Biostatistics, 6 (1), 1–6. doi:https://doi.org/10.4172/2155-6180.1000224.
- Semiromi, M.T. and Koch, M., 2019. Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain, Iran. Hydrological Sciences Journal, 64 (14), 1711–1726. doi:https://doi.org/10.1080/02626667.2019.1669793.
- Su, Y.-S., et al., 2011. Multiple imputation with diagnostics (mi) in R: opening windows into the black box. Journal of Statistical SoftwareSoftware, 45 (2), 31. doi:https://doi.org/10.18637/jss.v045.i02.
- Tencaliec, P., et al., 2015. Reconstruction of missing daily streamflow data using dynamic regression models. Water Resources Research, American Geophysical Union, 51 (12), 9447–9463. doi:https://doi.org/10.1002/2015WR017399.
- Tencaliec, P., 2017. Developments in statistics applied to hydrometeorology: imputation of streamflow data and semiparametric precipitation modeling. Universite Grenoble Alpes.
- Tyralis, H., Papacharalampous, G., and Langousis, A., 2019. A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water, 11 (5), 1–37. doi:https://doi.org/10.3390/w11050910.
- van Buuren, S., 2007. Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research, 16 (3), 219–242. doi:https://doi.org/10.1177/0962280206074463.
- van Buuren, S. and Groothuis-Oudshoorn, K., 2011. mice : Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45 (3), 1–67. doi:https://doi.org/10.18637/jss.v045.i03.
- Van Loon, A.F. and Laaha, G., 2015. Hydrological drought severity explained by climate and catchment characteristics. Journal of Hydrology, 526, 3–14. doi:https://doi.org/10.1016/j.jhydrol.2014.10.059
- Vezza, P., et al., 2010. Low flows regionalization in north-western Italy. Water Resources Management, 24 (14), 4049–4074. doi:https://doi.org/10.1007/s11269-010-9647-3.
- White, I.R. and Wood, A.M., 2011. Multiple imputation using chained equations : issues and guidance for practice. Statistics in Medicine, 30 (4), 377–399. doi:https://doi.org/10.1002/sim.4067.
- 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. doi:https://doi.org/10.1111/j.1540-5834.2006.00404.x.
- Yang, H.H., et al., 2011. Analysis of hydrological processes of Langat River sub basins at Lui and Dengkil. International Journal of the Physical Sciences, 6 (32), 7390–7409. doi:https://doi.org/10.5897/IJPS11.1036.
- Zhao, Y. and Long, Q., 2016. Multiple imputation in the presence of high-dimensional data. Statistical Methods in Medical Research, 25 (5), 2021–2035. doi:https://doi.org/10.1177/0962280213511027.
- Zvarevashe, W., Krishnannair, S., and Sivakumar, V., 2019. Analysis of rainfall and temperature data using ensemble empirical mode decomposition. Data Science Journal, 18 (1), 1–9. doi:https://doi.org/10.5334/dsj-2019-046.