2020
DOI: 10.1016/j.jhydrol.2020.125531
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Evaluating the performance of random forest for large-scale flood discharge simulation

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Cited by 128 publications
(45 citation statements)
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“…The accuracy of Random Forest is similar to (or even better than) other studies [33,42,43]. The glacier movement provided important information for understanding glacial changes.…”
Section: Comparison With Previous Glacier Classification Methods and supporting
confidence: 72%
“…The accuracy of Random Forest is similar to (or even better than) other studies [33,42,43]. The glacier movement provided important information for understanding glacial changes.…”
Section: Comparison With Previous Glacier Classification Methods and supporting
confidence: 72%
“…This ensemble, tree-based method has seen previous applications in this field of study. In particular, RF models in the field of hydrology have proven useful in flood risk analysis and susceptibility mapping (Zhao et al 2018), rainfall forecasting (Taksande and Mohod 2015) with performance close to that of Support Vector Machines (Yu et al 2017;Mosavi et al 2018), and, in recent studies, seen as advantageous in large-scale flood discharge simulations (Schoppa et al 2020). These models are less prone to overfitting since an increasing number of base learners leads to a converging generalization error (see Theorem 1.2 in Breiman 2001).…”
Section: Development Of Predictive Modelsmentioning
confidence: 99%
“…The potential for ML-based approaches towards simulating streamflow among other hydrologic applications (Elshorbagy et al 2010;Kasiviswanathan et al 2016;Schoppa et al 2020) has been noted for well over a decade now, but broader exploration was attempted only recently. Over the last few years, the availability of large-sample, high-resolution, observed and simulated hydrometeorological datasets have enabled the analysis of various flood generation processes at the catchment scale along with their drivers.…”
Section: Introductionmentioning
confidence: 99%
“…However, the frequency peak in the training was concentrated close to 0.020 m 3 s -1 km -2 , while in validation was close to 0.025 m 3 s -1 km -2 in the Qm prediction by the RF model. Thus, the training in samples with a greater peak in lower flow rates could explain the overestimation in the validation prediction, as the predictive power of RF completely depends on the training experience (Schoppa et al, 2020).…”
Section: Figure 13mentioning
confidence: 99%
“…The use of RF in water resources is recent (Tyralis et al, 2019). The RF has been using on water price prediction (Xu et al, 2019), regionalization of hourly hydrological model parameters (Saadi et al, 2019), large-scale flood discharge simulation (Schoppa et al, 2020), and several hydrological parameters and signatures (Booker and Woods, 2014;Addor et al, 2018;Booker and Snelder, 2012). However, few studies focused on applying RF to predict specific quantiles along the flow duration curve (for example, Schnier and Cai, 2014).…”
Section: Introductionmentioning
confidence: 99%