Evaluation of Random Forest for short-term daily streamflow forecast in rainfall and snowmelt driven watersheds

Abstract

Abstract. In the past decades, data-driven Machine Learning (ML) models have emerged as promising tools for short-term streamflow forecasts. Among other qualities, the popularity of ML for such applications is due to the methods' competitive performance compared with alternative approaches, ease of application, and relative lack of strict distributional assumptions. Despite the encouraging results, most applications of ML for streamflow forecast have been limited to watersheds where rainfall is the major source of runoff. In this study, we evaluate the potential of Random Forest (RF), a popular ML method, to make streamflow forecast at 1-day lead time at 86 watersheds in the Pacific Northwest. These watersheds span climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes: rainfall-dominated, transisent, and snowmelt-dominated based on the timing of center of annual flow volume. RF performance is benchmarked against Naive and multiple linear regression (MLR) models, and evaluated using four metrics Coefficient of determination, Root mean squared error, Mean absolute error, and Kling-Gupta efficiency. Model evaluation metrics suggest RF performs better in snowmelt-driven watersheds. Largest improvement in forecasts, compared to benchmark models, are found among rainfall-driven watersheds. We obtain Kling–Gupta Efficiency (KGE) scores in the range of 0.62–0.99. RF performance deteriorates with increase in catchment slope and increase in soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model

“…e random forest (RF) is one of the most powerful algorithms of ML for data science, which has been widely used in the construction field [14]. e RF model is successfully applied to solve numerous technical issues of civil engineering [15][16][17][18], geotechnical engineering [19][20][21][22], earth sciences [23][24][25], and environmental protection [26,27]. For example, Mohana [17] has used the RF model and 268 experimental data to predict the compressive strength of concrete containing GGBFS.…”

Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections

“…RFs are appealing for a multitude of reasons, including their predictive performance, robustness, speed, nonparametric nature, stability, diagnosis of variable importance, as well as their ability to handle nonlinearity, interactions, noise, and small sample sizes in forcing data (Tyralis et al., 2019). RFs are being used in a variety of applications including water resources and water quality modeling (Suchetana et al., 2017), construction safety risk (Tixier et al., 2016), and used with success in recent years for flow forecasting, primarily at daily and monthly time scales (Abbasi et al., 2021; Al‐Juboori, 2019; Ghorbani et al., 2020; Hussain & Khan, 2020; Li et al., 2019; Liang et al., 2018; Muñoz et al., 2018; Papacharalampous & Tyralis, 2018; Pham et al., 2020).…”

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

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