References
- Abbas, S.A., Xuan, Y., and Song, X., 2019. Quantile regression based methods for investigating rainfall trends associated with flooding and drought conditions. Water Resources Management, 33 (12), 4249–4264. doi:https://doi.org/10.1007/s11269-019-02362-0
- Abdi, A.M., 2020. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57 (1), 1–20. doi:https://doi.org/10.1080/15481603.2019.1650447
- Aghelpour, P., Bahrami-Pichaghchi, H., and Varshavian, V., 2021. Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran. Stochastic Environmental Research and Risk Assessment, 35 (8), 1615–1635. doi:https://doi.org/10.1007/s00477-020-01949-z
- Akoa, F.B., 2008. Combining DC Algorithms (DCAs) and Decomposition Techniques for the Training of Nonpositive–Semidefinite Kernels. IEEE Transactions on Neural Networks, 19 (11), 1854–1872. doi:https://doi.org/10.1109/TNN.2008.2003299
- Ali, I., et al., 2015. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sensing, 7 (12), 16398–16421. doi:https://doi.org/10.3390/rs71215841
- Aouissi, J., et al., 2013. Sensitivity analysis of SWAT model to the spatial rainfall distribution and watershed subdivision in streamflow simulations in the Mediterranean context: a case study in the Joumine watershed. Tunisia. Conf. Model. Simul. Appl. Optim. ICMSAO, 2013(1), 3–8, https://doi.org/10.1109/ICMSAO.2013.6552706, 2013 5th Int
- Arnold, J.G., et al., 2012. SWAT: model use, calibration, and validation. Transactions of the ASABE, 55 (4), 1491–1508. American Society of Agricultural and Biological Engineers: https://doi.org/10.13031/2013.42256.
- Arsenault, R. and Brissette, F., 2016. Analysis of continuous streamflow regionalization methods within a virtual setting. Hydrological Sciences Journal, 61 (15), 2680–2693. doi:https://doi.org/10.1080/02626667.2016.1154557
- Bafitlhile, T.M. and Li, Z., 2019. Applicability of ε-support vector machine and artificial neural network for flood forecasting in humid, semi-humid and semi-arid basins in China. Water, MDPI, 11 (1). doi:https://doi.org/10.3390/w11010085
- Chen, T. and Guestrin, C., 2016. XGBoost: A scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 785–794.
- Chen, T., He, T., and Benesty, M., 2020. Xgboost: extreme gradient boosting. cran.fhcrc.org. http://cran.fhcrc.org/web/packages/xgboost/vignettes/xgboost.pdf [Accessed 23 June 2020].
- Chibanga, R., Berlamont, J., and Vandewalle, J., 2003. Modelling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin. Hydrological Sciences Journal, 48 (3), 363–380. doi:https://doi.org/10.1623/hysj.48.3.363.45282
- Dehghani, R. and Poudeh, H.T., 2020. Application of support vector machine for river flow estimation. International Journal Of Engineering, Business And Management, 4 (2), 29–38. doi:https://doi.org/10.22161/ijebm.4.2.1
- Fan, J., et al., 2018. Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Conversion and Management, 164, 102–111. doi:https://doi.org/10.1016/j.enconman.2018.02.087
- Hadi, S.J., et al., 2019. Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation. IEEE Access, 7, 141533–141548. doi:https://doi.org/10.1109/ACCESS.2019.2943515
- Halefom, A., et al., 2017. Hydrological modeling of urban catchment using semi-distributed model. Modeling Earth Systems and Environment, 3(2), 683–692, https://doi.org/10.1007/s40808-017-0327-7, Springer International Publishing
- Hyndman, R.J. and Fan, Y., 1996. Sample quantiles in statistical packages. Am. Stat. doi:https://doi.org/10.1080/00031305.1996.10473566
- Karandish, F. and Šimůnek, J., 2016. A comparison of numerical and machine-learning modeling of soil water content with limited input data. Journal of Hydrology, 543, 892–909. doi:https://doi.org/10.1016/j.jhydrol.2016.11.007
- Khare, D., et al., 2015. Assessment of surface runoff in a Barinallah watershed using distributed parameter model (SWAT Model). J. Water Resour. Environ. Eng, 1 (1), 31–38. https://www.researchgate.net/profile/Nitin-Mishra-5/publication/279952921_Assessment_of_Surface_Runoff_in_a_Barinallah_Watershed_using_Distributed_Parameter_Model_SWAT_Model/links/559f81d408ae02b96ae605da/Assessment-of-Surface-Runoff-in-a-Barinallah-Watershed-using-Distributed-Parameter-Model-SWAT-Model.pdf?origin=publication_detail
- Kişi, O. and Çimen, M., 2009. Evapotranspiration modelling using support vector machines . / Modélisation de L’évapotranspiration À L’aide de ‘Support Vector Machines’. Hydrological Sciences Journal, 54 (5), 918–928. doi:https://doi.org/10.1623/hysj.54.5.918
- Kundu, S., et al., 2015. Analysis of spatial and temporal variation in rainfall trend of Madhya Pradesh, India (1901–2011). Environmental Earth Sciences, 73 (12), 8197–8216. doi:https://doi.org/10.1007/s12665-014-3978-y
- Lin, J.-Y., Cheng, C.-T., and Chau, K.-W., 2006. Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 51(4), 599–612. https://doi.org/10.1623/hysj.51.4.599, Taylor & Francis Group
- Malunjkar, V.S., et al., 2015. Estimation of surface runoff using SWAT Model. Int. J. Inven. Eng. Sci, 3 (4), 12–15.
- Martí, P., 2018. Discussion of “Estimating evapotranspiration using an extreme learning machine model: case Study in North Bihar, India” by Deepak Kumar, Jan Adamowski, Ram Suresh, and Bogdan Ozga-Zielinski. Journal of Irrigation and Drainage Engineering, 144 (5), 07018017. doi:https://doi.org/10.1061/(asce)ir.1943-4774.0001288
- Meenal, R. and Selvakumar, A.I., 2018. Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters Elsevier Ltd. Renewable Energy, 121, 324–343. doi:https://doi.org/10.1016/j.renene.2017.12.005
- Moriasi, D.N., et al., 2015. Hydrologic and water quality models: performance measures and evaluation criteria. Trans. ASABE, 58 (6), 1763–1785. doi:https://doi.org/10.13031/trans.58.10715
- Pajari, M., Tikanmäki, M., and Makkonen, L., 2019. Probabilistic evaluation of quantile estimators. Commun. Stat. - Theory Methods, 50(14), 1–19, https://doi.org/10.1080/03610926.2019.1696975, Taylor & Francis
- Parajka, J., Merz, R., and Blöschl, G., 2005. A comparison of regionalisation methods for catchment model parameters. Hydrology and Earth System Sciences, 9 (3), 157–171. doi:https://doi.org/10.5194/hess-9-157-2005
- Pendrill, F., Martin Persson, U., and Vadrevu, K.P., 2017. Combining global land cover datasets to quantify agricultural expansion into forests in Latin America: limitations and challenges. PLoS One, 12 (7), e0181202. (K. P. Vadrevu, Ed.) (), . Public Library of Science. https://doi.org/10.1371/journal.pone.0181202
- Prieto, C., et al., 2019. Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55 (5), 4364–4392. doi:https://doi.org/10.1029/2018WR023254
- Pruski, F.F., et al. 2013. Improved regionalization of streamflow by use of the streamflow equivalent of precipitation as an explanatory variable Elsevier B.V. Journal of Hydrology, 476, 52–71. doi:https://doi.org/10.1016/j.jhydrol.2012.10.005
- Rahman, A.T.M.S., et al., 2020. Multiscale groundwater level forecasting: coupling new machine learning approaches with wavelet transforms. Adv. Water Resour, 141. doi:https://doi.org/10.1016/j.advwatres.2020.103595
- Romero, E. and Toppo, D., 2007. Comparing support vector machines and feedforward neural networks with similar hidden-layer weights. IEEE Transactions on Neural Networks, 18 (3), 959–963. doi:https://doi.org/10.1109/TNN.2007.891656
- Simić, Z., et al., 2009. SWAT-based runoff modeling in complex catchment areas – theoretical background and numerical procedures. J. Serbian Soc. Comput. Mech, 3 (1), 38–63.
- Sisay, E., et al., 2017. Hydrological modelling of ungauged urban watershed using SWAT model. Modeling Earth Systems and Environment, 3(2), 693–702, https://doi.org/10.1007/s40808-017-0328-6, Springer International Publishing
- Song, J.H., et al., 2019. Regionalization of a rainfall-runoff model: limitations and potentials. Water (Switzerland), 11 (11), 10–12. doi:https://doi.org/10.3390/w11112257
- Srinivasan, R., Zhang, X., and Arnold, J., 2010. Swat ungauged: hydrological budget and crop yield predictions in the upper mississippi river basin. Am. Soc. Agric. Biol. Eng, 53 (5), 1533–1546. doi:https://doi.org/10.13031/2013.34903
- Swain, J.B. and Patra, K.C., 2017. Streamflow estimation in ungauged catchments using regionalization techniques Elsevier B.V. Journal of Hydrology, 554, 420–433. doi:https://doi.org/10.1016/j.jhydrol.2017.08.054
- Swain, J.B. and Patra, K.C., 2019. Impact of catchment classification on streamflow regionalization in ungauged catchments. SN Applied Sciences, 1 (5), 1–14. doi:https://doi.org/10.1007/s42452-019-0476-6
- Vapnik, V., 2013. The nature of statistical learning theory. https://books.google.com/books?hl=en&lr=&id=EqgACAAAQBAJ&oi=fnd&pg=PR7&ots=g3M0etaWa8&sig=Pr2riML3rPSL7glcJooEZz6ZVdk [Accessed 23 June 2020].
- Viviroli, D. and Seibert, J., 2015. Can a regionalized model parameterisation be improved with a limited number of runoff measurements? Journal of Hydrology, 529 (1), 49–61. doi:https://doi.org/10.1016/j.jhydrol.2015.07.009
- Wale, A., et al., 2009. Ungauged catchment contributions to Lake Tana’s water balance. Hydrol. Process, 23 (26), 3682–3693. doi:https://doi.org/10.1002/hyp.7284
- Wang, J., Feng, K., and Wu, J., 2019. SVM-based deep stacking networks. 33rd AAAI Conf. Artif. Intell. AAAI 2019, 31st Innov. Appl. Artif. Intell. Conf. IAAI 2019 9th AAAI Symp. Educ. Adv. Artif. Intell. EAAI 2019. Honolulu, Hawaii, USA. 5273–5280. doi:https://doi.org/10.1609/aaai.v33i01.33015273
- Yang, X., et al., 2018. Runoff prediction in ungauged catchments in Norway: comparison of regionalization approaches. Hydrology Research, 49 (2), 487–505. doi:https://doi.org/10.2166/nh.2017.071
- Yao, Y., et al., 2017. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242, 55–74. doi:https://doi.org/10.1016/j.agrformet.2017.04.011
- Yen, H., et al., 2016. Application of large-scale, multi-resolution watershed modeling framework using the hydrologic and water quality system (HAWQS). Water, 8 (4), 164. doi:https://doi.org/10.3390/w8040164
- You, F.T., Linger, D., and Wild, F.G., 2011. Five things you should know about funds. Pap. SAS525-2017 2–6. https://support.sas.com/resources/papers/proceedings17/SAS0525-2017.pdf [Accessed 21 January 2020].
- Zeiger, S.J. and Hubbart, J.A., 2016. A SWAT model validation of nested-scale contemporaneous stream flow, suspended sediment and nutrients from a multiple-land-use watershed of the central USA. Science of the Total Environment, 572, 232–243. doi:https://doi.org/10.1016/j.scitotenv.2016.07.178
- Zeiger, S. and Hubbart, J., 2017. An assessment of mean areal precipitation methods on simulated stream flow: a SWAT model performance assessment. Water, MDPI, 9 (7). doi:https://doi.org/10.3390/w9070459
- Zhang, X., et al., 2011. Simultaneous calibration of surface flow and baseflow simulations: a revisit of the SWAT model calibration framework. Hydrological Processes, 25 (14), 2313–2320. doi:https://doi.org/10.1002/hyp.8058