Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches

Roushangar, Kiyoumars; Shahnazi, Saman

doi:10.1007/s11629-018-5156-2

Cited by 15 publications

(5 citation statements)

References 35 publications

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“…As a useful non-parametric ML technique, GP can be applied to build comprehensive probabilistic models of real-world issues. A GP is a stochastic procedure whose substantiation is constituted of randomized amounts which are related with every point in spatial and temporal scales such that every random variable is normally distributed (Roushangar and Shahnazi 2020). In addition, every finite set of those random variables is characterized with a multivariate normal distribution.…”

Section: Gaussian Process (Gps)mentioning

confidence: 99%

Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

Asadi

Fathzadeh²,

Kerry

et al. 2021

Arab J Geosci

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Estimating sediment load of rivers is one of the major problems in river engineering that has been using various data mining algorithms and variables. It is desirable to obtain accurate estimates of sediment load while using techniques that limit computational intensity when datasets are large. This study investigates the usefulness of geo-morphometric factors and machine learning (ML) models for predicting suspended sediment load (SSL) in several river basins in Lorestan and Gilan, Iran. Six ML models, namely, multiple linear regression (MLR), artificial neural networks (ANN), K-nearest neighbor (KNN), Gaussian processes (GP), support vector machines (SVM), and evolutionary support vector machines (ESVM), were evaluated for estimating minimum and average SSL for the study regions. Geo-morphometric parameters and river discharge data were utilized as the main predictors in modeling process. In addition, an attribute reduction technique was applied to decrease the algorithm complexity and computational resources used. The results showed that all models estimated both target variables well. However, the optimal models for predicting average sediment load and minimum sediment load were the GP and ESVM models, respectively.

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Section: Gaussian Process (Gps)mentioning

confidence: 99%

Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

Asadi

Fathzadeh²,

Kerry

et al. 2021

Arab J Geosci

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“…Some of the kernel functions and their parameters are listed in Table 2. In this study, radial basis function (RBF) kernel function was used for implementation of SVM algorithm as suggested by researchers (Azamathulla et al 2016;Roushangar & Shahnazi 2020). The optimum values of RBF kernel parameter (γ) were obtained after trial and error process.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions

Roushangar

Shahnazi

2021

Journal of Hydroinformatics

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Ubiquitous flow bedforms such as ripples in rivers and coastal environments can affect the transport conditions as they constitute the bed roughness elements. The roughness coefficient needs to be adequately quantified owing to its significant influence on the performance of hydraulic structures and river management. This work intended to evaluate the sensitivity and robustness of three machine learning (ML) methods, namely, Gaussian process regression (GPR), artificial neural network (ANN), and support vector machine (SVM) for the prediction of the Manning roughness coefficient of channels with ripple bedforms. To this purpose, 840 experimental data points considering various hydraulic conditions were prepared. According to the obtained results, GPR was found to accurately predict the Manning's coefficient with input parameters of Reynolds number (Re), depth to width ratio (y/b), the ratio of the hydraulic radius to the median grain diameter (R/D50), and grain Froude number (). Moreover, sensitivity analysis was implemented with proposed ML approaches which indicated that the ratio of the hydraulic radius to the median grain diameter has a considerable role in modeling the Manning's coefficient in channels with ripple bedforms.

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“…The data related to sediment concentrations along with water discharges are collected according to United States Geological Survey (USGS) specifications for random days throughout the year at controlled section of the river [35][36][37]. Therefore, to predict the missing sediment data, researchers employed different methods i.e., the conventional sediment rating curves (SRCs) method [38] and the artificial neural networking (ANN) technique [39].…”

Section: Sediment Datamentioning

confidence: 99%

“…A number of researchers predicted the hydrological parameter by developing artificial neural network (ANN)-based models [47,48]. For many years, ANN-based models have been used for rainfall estimation, runoff estimation, reservoir inflow prediction, suspended sediment prediction, reservoir level estimation, and reservoir operation [39,[49][50][51][52]. In this study, missing and future suspended sediment data were predicted by ANN-based data-driven technique for the efficient estimation of incoming suspended sediment.…”

Section: Artificial Neural Network Model (Ann)mentioning

confidence: 99%

Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan

Khan

Stamm

Haider

2020

Water

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Reservoir sedimentation reduces the gross storage capacity of dams and also negatively impacts turbine functioning, posing a danger to turbine inlets. When the sediment delta approaches the dam, further concerns arise regarding sediments passing through turbine intakes, blades abrasion due to increased silt/sand concentration, choking of outlets, and dam safety. Thus, slowing down the delta advance rate is a worthy goal from a dam manager’s viewpoint. These problems can be solved through a flexible reservoir operation strategy that prioritize sediment deposition further away from the dam face. As a case study, the Mangla Reservoir in Pakistan is selected to elaborate the operational strategy. The methodology rests upon usage of a 1D sediment transport model to quantify the impact of different reservoir operating strategies on sedimentation. Further, in order to assess the long-term effect of a changing climate, a global climate model under representative concentration pathways scenarios 4.5 and 8.5 for the 21st century is used. The reduction of uncertainty in the suspended sediments concentration is achieved by employing an artificial neural networking technique. Moreover, a sensitivity analysis focused on estimating the impact of various parameters on sediment transport modelling was conducted. The results show that a gradual increase in the reservoir minimum operating level slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of a sustainable reservoir use for the societal benefit.

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Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches

Cited by 15 publications

References 35 publications

Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions

Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan

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