Adi-Kusumo, F., Gunardi, Utami, H., Nurjani, E., Sopaheluwakan, A., Aluicius, I. E., & Christiawan, T. (2016). Application of the empirical orthogonal function to study the rainfall pattern in daerah istimewa yogyakarta province. Paper presented at the AIP Conference Proceedings, , 1707 doi:10.1063/1.4940833 Retrieved from www.scopus.com
Aguilera, H., Guardiola-Albert, C., Serrano-Hidalgo, C., & Naranjo-Fernández, N. (2018). Estimating large amounts of missing precipitation data. EGU General Assembly Conference Abstracts, 22, 578-592. Retrieved from www.scopus.com
Aldrian, E., & Dwi Susanto, R. (2003). Identification of three dominant rainfall regions within indonesia and their relationship to sea surface temperature. International Journal of Climatology, 23(12), 1435-1452. doi:10.1002/joc.950
Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 46(3), 175-185. doi:10.1080/00031305.1992.10475879
Amiri, M. A., Conoscenti, C., & Mesgari, M. S. (2018). Improving the accuracy of rainfall prediction using a regionalization approach and neural networks. Kuwait Journal of Science, 45(4), 66-75. Retrieved from www.scopus.com
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? -arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250. doi:10.5194/gmd-7-1247-2014
Dai, Q., Bray, M., Zhuo, L., Islam, T., & Han, D. (2017). A scheme for rain gauge network design based on remotely sensed rainfall measurements. Journal of Hydrometeorology, 18(2), 363-379. doi:10.1175/JHM-D-16-0136.1
Darand, M., & Mansouri Daneshvar, M. R. (2014). Regionalization of precipitation regimes in iran using principal component analysis and hierarchical clustering analysis. Environmental Processes, 1(4), 517-532. doi:10.1007/s40710-014-0039-1
Dodeen, H. M. (2003). Effectiveness of valid mean substitution in treating missing data in attitude assessment. Assessment and Evaluation in Higher Education, 28(5), 505-513. doi:10.1080/02602930301674
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1-26. Retrieved from www.scopus.com
Efron, B., & Tibshirani, R. J. (1990). Regression Model, an Introduction to the Bootstrap, Retrieved from www.scopus.com
Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. An Introduction to Applied Multivariate Analysis with R, Retrieved from www.scopus.com
Goyal, M. K., & Gupta, V. (2014). Identification of homogeneous rainfall regimes in northeast region of india using fuzzy cluster analysis. Water Resources Management, 28(13), 4491-4511. doi:10.1007/s11269-014-0699-7
Gupta, A., Kamble, T., & Machiwal, D. (2017). Comparison of ordinary and bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west india. Environmental Earth Sciences, 76(15) doi:10.1007/s12665-017-6814-3
Haines, H. A., & Olley, J. M. (2017). The implications of regional variations in rainfall for reconstructing rainfall patterns using tree rings. Hydrological Processes, 31(16), 2951-2960. doi:10.1002/hyp.11238
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2-3), 107-145. doi:10.1023/A:1012801612483
Harel, O., & Zhou, X. -. (2007). Multiple imputation: Review of theory, implementation and software. Statistics in Medicine, 26(16), 3057-3077. doi:10.1002/sim.2787
Heras, D., & Matovelle, C. (2021). Machine-learning methods for hydrological imputation data: Analysis of the goodness of fit of the model in hydrographic systems of the pacific - ecuador. [Métodos de aprendizado de máquina para dados de imputação hidrológica: Análise da qualidade de ajuste do modelo em sistemas hidrográficos do Pacífico - Equador] Revista Ambiente e Agua, 16(3) doi:10.4136/ambi-agua.2708
Hoyos, L. E., Cabido, M. R., & Cingolani, A. M. (2018). A multivariate approach to study drivers of land-cover changes through remote sensing in the dry chaco of argentina. ISPRS International Journal of Geo-Information, 7(5) doi:10.3390/ijgi7050170
Ibarra-Berastegi, G., Saénz, J., Ezcurra, A., Elías, A., Diaz Argandoña, J., & Errasti, I. (2011). Downscaling of surface moisture flux and precipitation in the ebro valley (spain) using analogues and analogues followed by random forests and multiple linear regression. Hydrology and Earth System Sciences, 15(6), 1895-1907. doi:10.5194/hess-15-1895-2011
Ismail, W. N. W., Zin, W. Z. W., & Ibrahim, W. (2017). Estimation of rainfall and stream flow missing data for terengganu, malaysia by using interpolation technique methods. Malaysian Journal of Fundamental and Applied Sciences, 13(3), 213-217. Retrieved from www.scopus.com
Jolliffe, I. T. (1986). Principal Component Analysis, Retrieved from www.scopus.com
Kamaruzaman, I. F., Zin, W. Z. W., & Ariff, N. M. (2017). A comparison of method for treating missing daily rainfall data in peninsular malaysia. Malaysian Journal of Fundamental and Applied Sciences, 13(4-1), 375-380. Retrieved from www.scopus.com
Kiviet, J. F. (1995). On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics, 68(1), 53-78. doi:10.1016/0304-4076(94)01643-E
Latupapua, H., Latupapua, A. I., Wahab, A., & Alaydrus, M. (2018). Wireless sensor network design for earthquake’s and landslide’s early warnings. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), 437-445. doi:10.11591/ijeecs.v11.i2.pp437-445
Lee, H. S. (2015). General rainfall patterns in indonesia and the potential impacts of local seas on rainfall intensity. Water (Switzerland), 7(4), 1751-1768. doi:10.3390/w7041751
Lin, G. -., Chang, M. -., & Wu, J. -. (2017). A hybrid statistical downscaling method based on the classification of rainfall patterns. Water Resources Management, 31(1), 377-401. doi:10.1007/s11269-016-1532-2
Logue, J. J. (1984). Regional variations in the annual cycle of rainfall in ireland as revealed by principal component analysis. Journal of Climatology, 4(6), 597-607. doi:10.1002/joc.3370040604
Lusajo, M., Salim, C. J., & Kazumba, S. (2018). Estimation of missing river flow data for hydrological analysis: The case of great ruaha river catchment. Hydrol.Current Res, 9(2), 1-8. Retrieved from www.scopus.com
Machiwal, D., Dayal, D., & Kumar, S. (2017). Long-term rainfall trends and change points in hot and cold arid regions of india. Hydrological Sciences Journal, 62(7), 1050-1066. doi:10.1080/02626667.2017.1303705
Machiwal, D., Kumar, S., Meena, H. M., Santra, P., Singh, R. K., & Singh, D. V. (2019). Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of india. Meteorological Applications, 26(2), 300-311. doi:10.1002/met.1763
Medina-Cobo, M. T., García-Marín, A. P., Estévez, J., Jiménez-Hornero, F. J., & Ayuso-Muñoz, J. L. (2017). Obtaining homogeneous regions by determining the generalized fractal dimensions of validated daily rainfall data sets. Water Resources Management, 31(7), 2333-2348. doi:10.1007/s11269-017-1653-2
Messakh, J. J., Arwin, S., Hadihardaja, I. K., & Dupe, Z. (2015). Management strategy of water resources base on rainfall characteristics in the semi-arid region in indonesia. International Journal of Scientific & Engineering Research, 8, 331-338. Retrieved from www.scopus.com
Modarres, R., & Sarhadi, A. (2011). Statistically-based regionalization of rainfall climates of iran. Global and Planetary Change, 75(1-2), 67-75. doi:10.1016/j.gloplacha.2010.10.009
Nagel, J. B., Rieckermann, J., & Sudret, B. (2020). Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation. Reliability Engineering and System Safety, 195 doi:10.1016/j.ress.2019.106737
Nor, S. M. C. M., Shaharudin, S. M., Ismail, S., & Kismiantini. (2021). A rpca-based tukey's biweight for clustering identification on extreme rainfall data. Environment and Ecology Research, 9(3), 114-118. doi:10.13189/eer.2021.090303
Rodríguez, R., Pastorini, M., Etcheverry, L., Chreties, C., Fossati, M., Castro, A., & Gorgoglione, A. (2021). Water-quality data imputation with a high percentage of missing values: A machine learning approach. Sustainability (Switzerland), 13(11) doi:10.3390/su13116318
Saputra, A., Gomez, C., Delikostidis, I., Zawar-Reza, P., Hadmoko, D. S., Sartohadi, J., & Setiawan, M. A. (2018). Determining earthquake susceptible areas southeast of yogyakarta, Indonesia—outcrop analysis from structure from motion (SfM) and geographic information system (GIS). Geosciences (Switzerland), 8(4) doi:10.3390/geosciences8040132
Shaharudin, S. M., & Ahmad, N. (2017). Choice of cumulative percentage in principal component analysis for regionalization of peninsular malaysia based on the rainfall amount doi:10.1007/978-981-10-6502-6_19 Retrieved from www.scopus.com
Shaharudin, S. M., Ahmad, N., & Nor, S. M. C. M. (2020). A modified correlation in principal component analysis for torrential rainfall patterns identification. IAES International Journal of Artificial Intelligence, 9(4), 655-661. doi:10.11591/ijai.v9.i4.pp655-661
Shaharudin, S. M., Andayani, S., Binatari, N., Kurniawan, A., Ahmad Basri, M. A., & Zainuddin, N. H. (2020). Imputation methods for addressing missing data of monthly rainfall in yogyakarta, indonesia. Int.J.Adv.Trends Comput.Sci.Eng, 9, 646-651. Retrieved from www.scopus.com
Shaharudin, S. M., Nor, S. M. C. M., Tan, M. L., Samsudin, M. S., Azid, A., & Ismail, S. (2021). Spatial torrential rainfall modelling in pattern analysis based on robust pca approach. Polish Journal of Environmental Studies, 30(4), 3221-3230. doi:10.15244/pjoes/130677
Ward, J. H., Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236-244. doi:10.1080/01621459.1963.10500845
Widagdo, A., Pramumijoyo, S., & Harijoko, A. (2018). Morphotectono-volcanic of MenorehGajah-ijo volcanic rock in western side of yogyakarta-indonesia. Journal of Geoscience, Engineering, Environment, and Technology, 3(3) Retrieved from www.scopus.com
Yang, J. -., Cheng, C. -., & Chan, C. -. (2017). A time-series water level forecasting model based on imputation and variable selection method. Computational Intelligence and Neuroscience, 2017 doi:10.1155/2017/8734214