Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads

Shiau, Jenq Tzong; Chen, Ting Ju

doi:10.1007/s11269-015-0971-5

Cited by 26 publications

(7 citation statements)

References 42 publications

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“…Sediment load and associated deposition may contribute to reducing reservoir volume, obstructing dam outlets and reducing channel carrying capacity (Buyukyildiz & Kumcu, 2017). Sediment also affects water quality, by transporting contaminants and potentially leading to reduction of dissolved oxygen concentrations (Shiau & Chen, 2015). Suspended Sediment Load (SSL) and Bed Load (BL) are the two components of the Total Sediment Load (TSL), and the former part has generally more complex characteristics compared to BL (Zounemat-Kermani et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Sharafati

Asadollah

Motta

et al. 2020

Hydrological Sciences Journal

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Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely Gradient Boost Regression (GBR), Ada Boost Regression (ABR) and Random Forest Regression (RFR) are proposed for prediction of Suspended Sediment Load (SSL), and their prediction performance and related uncertainty are assessed. The Suspended Sediment Load (SSL) of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river Discharge (Q) and Suspended Sediment Concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.

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Section: Introductionmentioning

confidence: 99%

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Sharafati

Asadollah

Motta

et al. 2020

Hydrological Sciences Journal

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“…This approach can capture the broad range of variability in measured data, particularly at high streamflows. Shiau and Chen (2015) applied this approach to a power law function sediment rating curve for a river in southern Taiwan. Theoretical details of quantile regression can be found in Koenker (2005).…”

Section: Methodsmentioning

confidence: 99%

“…It also produces a single, global regression for different levels of streamflow, enabling a more straightforward approach to extrapolation under projected streamflow values that may exceed the range of historical values. Quantile regression has already been successfully used to model sediment-streamflow relationships (Shiau and Chen, 2015) but has not been used for long-term planning applications under changing climate conditions. This study provides the first demonstration of such an application, using the Esopus Creek and New York City (NYC) water supply system as a case study.…”

Section: Introductionmentioning

confidence: 99%

“…Since its original development (Koenker and Bassett, 1978), quantile regression has been adapted as a model for various fields such as economics (Alagidede and Panagiotidis, 2012), biology and ecology (Cade and Noon, 2003), and environmental science (Shiau and Chen, 2015). Quantile regression addresses issues of uncertainty and scatter in the streamflow–turbidity relationship with the use of probabilistic estimations that account for a large variety of streamflow events.…”

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confidence: 99%

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Probabilistic Estimation of Stream Turbidity and Application under Climate Change Scenarios

Mukundan¹,

Scheerer

Gelda³

et al. 2018

J of Env Quality

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Streamflow‐based rating curves are widely used to estimate turbidity or suspended sediment concentrations in streams. However, such estimates are often inaccurate at the event scale due to inter‐ and intra‐event variability in sediment–streamflow relationships. In this study, we use a quantile regression approach to derive a probabilistic distribution of turbidity predictions for Esopus Creek, a major stream in one of the watersheds that supply drinking water to New York City, using measured daily mean streamflow–turbidity data pairs for 2003 to 2016. Although a single regression curve can underpredict or overpredict the actual observation, quantile regression can estimate a range of possible turbidity values for a given value of streamflow. Regression relationships for various quantiles were applied to streamflows simulated by a watershed model to predict stream turbidity under: (i) the observed historical climate, and (ii) a future climate derived from 20 global climate model (GCM) scenarios. Future scenarios using quantile regression in combination with these GCMs and a stochastic weather generator indicated an increase in the frequency and magnitude of hydrological events that may generate high stream turbidity and cause potential water quality challenges to the water supply. The methods outlined in this study can be used for probabilistic estimation of stream turbidity for operational decisions and can be part of a vulnerability‐based method to explore climate impacts on water resources. Core Ideas Quantile regression addresses the uncertainty in the streamflow–turbidity relationship. Stochastic weather generator incorporates climate variability in climate change impact analysis. Stream turbidity under future climate for a New York City watershed is presented. Future scenarios show increase in the frequency and magnitude of high‐stream‐turbidity events.

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“…Shiau and Chen [19] applied a probabilistic system for forecasting daily-suspended loads. The accuracy of the probabilistic model was better than experimental rating curve methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions

et al. 2019

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Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS–BA, ANFIS–WA, MFNN–BA, and MFNN–WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash–Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS–BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0. 75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS–BA had more reliable performance compared to other models. Thus, the ANFIS–BA model has high potential for predicting SSL.

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Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads

Cited by 26 publications

References 42 publications

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Probabilistic Estimation of Stream Turbidity and Application under Climate Change Scenarios

Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions

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