References
- Asefa, T., Kemblowski, M., McKee, M., Khalil, A., 2006. Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology, 318, 1–4, 7–16.10.1016/j.jhydrol.2005.06.001
- Basak, D., Pal, S., Patranabis, D.C., 2007. Support vector regression. Neural Information Processing-Letters and Reviews, 11, 10, 203–224.
- Baudron, P., Alonso-Sarría, F., García-Aróstegui, J.L., Canovas-Garcia, F., Martinez-Vicente, D., Moreno-Brotons, J., 2013. Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification. Journal of Hydrology, 499, 303–315.10.1016/j.jhydrol.2013.07.009
- Ben-Hur, A., Weston, J., 2010. A user’s guide to support vector machines. In: Carugo, O., Eisenhaber, F. (Eds.): Data Mining Techniques for the Life Sciences. Methods in Molecular Biology (Methods and Protocols), Vol 609. Humana Press, 2010, pp. 223–239.
- Biswal, B., Kumar, D.N., 2014. What mainly controls recession flows in river basins? Advances in water resources, 65, 25–33.10.1016/j.advwatres.2014.01.001
- Blöschl, G., Blaschke, A.P., Broer, M., Bucher, C., Carr, G., Chen, X., Eder, A., Exner-Kittridge, M., Farnleitner, A., Flores-Orozco, A., Haas, P., Hogan, P., Kazemi Amiri, A., Oismüller, M., Parajka, J., Silasari, R., Stadler, P., Strauss, P., Vreugdenhil, M., Wagner, W., Zessner, M., 2016. The Hydrological Open Air laboratory (HOAL) in Petzenkirchen: a hypothesis-driven observatory. Hydrology and Earth System Sciences, 20, 1, 227.10.5194/hess-20-227-2016
- Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., Savenije, H., 2013. Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales. Cambridge University Press, Cambridge.10.1017/CBO9781139235761
- Blume, T., Zehe, E., Bronstert, A., 2007. Rainfall–runoff response, event-based runoff coefficients and hydrograph separation. Hydrological Sciences Journal, 52, 5, 843–862.10.1623/hysj.52.5.843
- Breiman, L., 2001. Random forests. Machine Learning, 45, 1, 5–32.10.1023/A:1010933404324
- Brutsaert, W., Nieber, J.L., 1977. Regionalized drought flow hydrographs from a mature glaciated plateau. Water Resources Research, 13, 3, 637–643.10.1029/WR013i003p00637
- Cánovas-García, F., Alonso-Sarría, F., Gomariz-Castillo, F., Onate-Valdivieso, F., 2017. Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery. Computers & Geosciences, 103, 1–11.10.1016/j.cageo.2017.02.012
- Chapelle, O., Vapnik, V., 2000. Model selection for support vector machines. In: NIPS’99 Proceedings of the 12th International Conference on Neural Information Processing Systems, Denver, CO, November 29 - December 4, 1999, pp. 230–236.
- Chapman, T.G., Maxwell, A.I., 1996. Baseflow separationcomparison of numerical methods with tracer experiments. In: Hydrology and Water Resources 23rd Symposium, Hobart, 1996. National Conference Publication – Institution of Engineers Australia NCP, 2(5), pp. 539–546.
- Chen, B., Krajewski, W.F., Helmers, M.J., Zhang, Z., 2019. Spatial variability and temporal persistence of event runoff coefficients for cropland hillslopes. Water Resources Research, 55, 2, 1583–1597.10.1029/2018WR023576
- Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine Learning, 20, 3, 273–297.10.1007/BF00994018
- Cortez, P., Embrechts, M.J., 2013. Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1–17.10.1016/j.ins.2012.10.039
- Deka, P.C., 2014. Support vector machine applications in the field of hydrology: a review. Applied Soft Computing, 19, 372–386.10.1016/j.asoc.2014.02.002
- Dietterich, T.G., 1997. Machine-learning research. AI Magazine, 18, 4, 97–97.
- Erdal, H.I., Karakurt, O., 2013. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. Journal of Hydrology, 477, 119–128.10.1016/j.jhydrol.2012.11.015
- Exner-Kittridge, M., Strauss, P., Blöschl, G., Eder, A., Saracevic, E., Zessner, M., 2016. The seasonal dynamics of the stream sources and input flow paths of water and nitrogen of an Austrian headwater agricultural catchment. Science of the Total Environment, 542, 935–945.10.1016/j.scitotenv.2015.10.151
- Friedman, J.H., 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 45, 1, 1189–1232.10.1214/aos/1013203451
- Friedman, J.H., 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38, 4, 367–378.10.1016/S0167-9473(01)00065-2
- Gaál, L., Szolgay, J., Kohnová, S., Parajka, J., Merz, R., Viglione, A., Blöschl, G., 2012. Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology. Water Resources Research, 48, W04511.10.1029/2011WR011509
- Gottschalk, L., Weingartner, R., 1998. Distribution of peak flow derived from a distribution of rainfall volume and runoff coefficient, and a unit hydrograph. Journal of Hydrology, 208, 3–4, 148–162.10.1016/S0022-1694(98)00152-8
- Hayes, D.C., Young, R.L., 2006. Comparison of peak discharge and runoff characteristic estimates from the rational method to field observations for small basins in Central Virginia. U.S. Department of the Interior, U.S. Geological Survey, Scientific Investigation Reports, 2005-5254.10.3133/sir20055254
- Ho, T.K., 1995. Random decision forests. In: ICDAR ‘95 Proceedings of the Third International Conference on Document Analysis and Recognition, 1, IEEE Computer Society Washington, DC, USA, 14–15 August 1995, pp. 278–282.
- Horn, R.A., Johnson, C.R., 1985. Matrix Analysis. Cambridge University Press, Cambridge.10.1017/CBO9780511810817
- Hsu, C.W., Chang, C.C., Lin, C.J., 2003. A practical guide to support vector classification. Technical Report. Department of Computer Science, National Taiwan University, Taipei.
- Hwang, S.H., Ham, D.H., Kim, J.H., 2012. Forecasting performance of LS-SVM for nonlinear hydrological time series. KSCE Journal of Civil Engineering, 16, 5, 870–882.10.1007/s12205-012-1519-3
- Krakauer, N.Y., Temimi, M., 2011. Stream recession curves and storage variability in small watersheds. Hydrology and Earth System Sciences, 15, 7, 2377–2389.10.5194/hess-15-2377-2011
- Liaw, A., Wiener, M., 2002. Classification and regression by random Forest. R News, 2, 3, 18–22.
- Longobardi, A., Villani, P., Grayson, R.B., Western, A., 2003. On the relationship between runoff coefficient and catchment initial conditions. In: Proceedings of MODSIM, pp. 867–872.
- Maity, R., Bhagwat, P.P., Bhatnagar, A., 2010. Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrological Processes, 24, 7, 917–923.10.1002/hyp.7535
- Merz, R., Blöschl, G., 2009. A regional analysis of event runoff coefficients with respect to climate and catchment characteristics in Austria. Water Resources Research, 45, 1, W01405.10.1029/2008WR007163
- Merz, R., Blöschl, G., Parajka, J., 2006. Spatio-temporal variability of event runoff coefficients. Journal of Hydrology, 331, 3–4, 591–604.10.1016/j.jhydrol.2006.06.008
- Naghibi, S.A., Ahmadi, K., Daneshi, A., 2017. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31, 9, 2761–2775.10.1007/s11269-017-1660-3
- Naghibi, S.A., Pourghasemi, H.R., Dixon, B., 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment, 188, 1, 44.10.1007/s10661-015-5049-626687087
- Norbiato, D., Borga, M., Merz, R., Blöschl, G., Carton, A., 2009. Controls on event runoff coefficients in the eastern Italian Alps. Journal of Hydrology, 375, 3–4, 312–325.10.1016/j.jhydrol.2009.06.044
- Osuna, E.E., 1998. Support vector machines: Training and applications. Diss. Massachusetts Institute of Technology.
- Palleiro, L., Rodríguez-Blanco, M.L., Taboada-Castro, M.M., Taboada-Castro, M.T., 2014. Hydrological response of a humid agroforestry catchment at different time scales. Hydrological Processes, 28, 4, 1677–1688.10.1002/hyp.9714
- Patnaik, S., Biswal, B., Kumar, D.N., Sivakumar, B., 2015. Effect of catchment characteristics on the relationship between past discharge and the power law recession coefficient. Journal of Hydrology, 528, 321–328.10.1016/j.jhydrol.2015.06.032
- Rodríguez-Blanco, M.L., Taboada-Castro, M.M., Taboada-Castro, M.T., 2012. Rainfall–runoff response and eventbased runoff coefficients in a humid area (northwest Spain). Hydrological Sciences Journal, 57, 3, 445–459.10.1080/02626667.2012.666351
- Sachdeva, S., Bhatia, T., Verma, A.K., 2018. GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer. International Society for the Prevention and Mitigation of Natural Hazards, 92, 3, 1399–1418.10.1007/s11069-018-3256-5
- Şen, Z., Altunkaynak, A., 2006. A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrological Processes, 20, 9, 1993–2009.10.1002/hyp.5992
- Shen, C., 2018. A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resources Research, 54, 8558–8593. https://doi.org/10.1029/2018WR02264310.1029/2018WR022643
- Sivapalan, M., 2003. Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrological Processes, 17, 15, 3163–3170.10.1002/hyp.5155
- Széles, B., Broer, M., Parajka, J., Hogan, P., Eder, A., Strauss, P., Blöschl, G., 2018. Separation of scales in transpiration effects on low flows: A spatial analysis in the Hydrological Open Air Laboratory. Water Resources Research, 54, https://doi.org/10.1029/2017WR022037.10.1029/2017WR022037
- Tachecí, P., Žlabek, P., Kvitek, T., Peterkova, J., 2013. Analysis of rainfall-runoff events in four subcatchments of the Kopaninský potok (Czech Republic). Bodenkultur, 64, 3–4, 105–111.
- Tague, C., Grant, G.E., 2004. A geological framework for interpreting the low-flow regimes of Cascade streams, Willamette River Basin, Oregon. Water Resources Research, 40, 4, W04303.10.1029/2003WR002629
- Tallaksen, L.M., 1995. A review of baseflow recession analysis. Journal of Hydrology, 165, 1–4, 349–370.10.1016/0022-1694(94)02540-R
- Tarasova, L., Basso, S., Poncelet, C., Merz, R., 2018a. Exploring controls on rainfall-runoff events: 2. Regional patterns and spatial controls of event characteristics in Germany. Water Resources Research, 54, https://doi.org/10.1029/2018WR02258810.1029/2018WR022588
- Tarasova, L., Basso, S., Zink, M., Merz, R. 2018b. Exploring controls on rainfall-runoff events: 1. Time-series-based event separation and temporal dynamics of event runoff response in Germany. Water Resources Research, 54, https://doi.org/10.1029/2018WR02258710.1029/2018WR022587
- Tian, F., Li, H., Sivapalan, M., 2012. Model diagnostic analysis of seasonal switching of runoff generation mechanisms in the Blue River basin, Oklahoma. Journal of Hydrology, 418, 136–149.10.1016/j.jhydrol.2010.03.011
- Vapnik, V., Golowich, S., Smola, A., 1997. Support vector method for function approximation, regression estimation, and signal processing. In: NIPS’96 Proceedings of the 9th International Conference on Neural Information Processing Systems, Denver, Colorado, 3–5 December 1996, pp. 281–287.
- Viglione, A., Merz, R., Blöschl, G., 2009. On the role of the runoff coefficient in the mapping of rainfall to flood return periods. Hydrology and Earth System Sciences, 13, 5, 577–593.10.5194/hess-13-577-2009
- Wainwright, J., Parsons, A.J., 2002. The effect of temporal variations in rainfall on scale dependency in runoff coefficients. Water Resources Research, 38, 12, 1271.
- Zimmermann, B., Zimmermann, A., Turner, B.L., Francke, T., Elsenbeer, H., 2014. Connectivity of overland flow by drainage network expansion in a rain forest catchment. Water Resources Research, 50, 2, 1457–1473.10.1002/2012WR012660