Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction

Fathabadi, Aboalhasan; Seyedian, Seyed Morteza; Malekian, Arash

doi:10.1016/j.scitotenv.2021.151760

Cited by 26 publications

(9 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GPR, commonly abbreviated as GPR in various communities, is a non-parametric Bayesian approach applicable to regression problems. The applications of GPR are diverse, not restricted to; structural engineering, 28,29 mechanical engineering, 30 environmental engineering, 31 sustainability, 32,33 bio-physics, 34 dynamic problems 35 and systems control. 36 Hence, GPR competes as an alternative to ANNs in numerous applications.…”

Section: Related Workmentioning

confidence: 99%

Nonlinear response modelling of material systems using constrained Gaussian processes

Herath,

Chakraborty

2024

Numerical Meth Engineering

View full text Add to dashboard Cite

This article investigates the suitability of constrained Gaussian process regression in predicting nonlinear mechanical responses of material systems with notably reduced uncertainties. This study reinforces the conventional Gaussian processes with mechanics‐informed prior knowledge observed in various kinematic responses. Stiffening and softening responses of material systems mostly demonstrate at least one of the boundedness, monotonicity and convexity conditions with respect to some kinematic variables. These relationships or impositions in turn are encoded into a constrained Gaussian process for prediction, uncertainty quantification and extrapolation. Using numerous examples and comparative studies, this article evidently proves that the use of constrained Gaussian processes is data‐efficient, highly accurate, yields low uncertainties, recovers model overfitting and extrapolates very well compared to unconstrained or conventional Gaussian processes. Moreover, the usability of the proposed numerical method across various engineering modelling domains such as multiscale homogenisation, experimentation, structural optimisation, material constitutive modelling and structural idealisation is demonstrated.

show abstract

Section: Related Workmentioning

confidence: 99%

Nonlinear response modelling of material systems using constrained Gaussian processes

Herath,

Chakraborty

2024

Numerical Meth Engineering

View full text Add to dashboard Cite

show abstract

“…The former measures the similarity between input vectors of observed and desired data points, while the latter is used to control the complexity of the model. Additionally, the covariance function is typically more important than the mean function [28]. The Radial Basis Function (RBF) kernel is commonly used as the covariance function in GPR models, which maps data to a high-dimensional space.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Application of Machine Learning Models to Bridge Afflux Estimation

Piraei,

Niazkar,

Afzali

et al. 2023

Water

View full text Add to dashboard Cite

Bridges are essential structures that connect riverbanks and facilitate transportation. However, bridge piers and abutments can disrupt the natural flow of rivers, causing a rise in water levels upstream of the bridge. The rise in water levels, known as bridge backwater or afflux, can threaten the stability or service of bridges and riverbanks. It is postulated that applications of estimation models with more precise afflux predictions can enhance the safety of bridges in flood-prone areas. In this study, eight machine learning (ML) models were developed to estimate bridge afflux utilizing 202 laboratory and 66 field data. The ML models consist of Support Vector Regression (SVR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Gradient Boost Regressor (GBR), eXtreme Gradient Boosting (XGBoost) for Regression (XGBR), Gaussian Process Regression (GPR), and K-Nearest Neighbors (KNN). To the best of the authors’ knowledge, this is the first time that these ML models have been applied to estimate bridge afflux. The performance of ML-based models was compared with those of artificial neural networks (ANN), genetic programming (GP), and explicit equations adopted from previous studies. The results show that most of the ML models utilized in this study can significantly enhance the accuracy of bridge afflux estimations. Nevertheless, a few ML models, like SVR and ABR, did not show a good overall performance, suggesting that the right choice of an ML model is important.

show abstract

“…The discharge value (Q) is obtained by multiplying the cross-sectional area (A) by the velocity. Details can be found in Equations ( 2) and (3) [46][47][48][49][50].…”

Section: The Measurement Of River Discharge (Q)mentioning

confidence: 99%

The Assessment of Sedimentation Problems in Kenyir Hydropower Reservoir, Malaysia

Wahab

Kamarudin

Toriman

et al. 2023

Water

View full text Add to dashboard Cite

Lakes may take a while to respond to management interventions because of the management implications of incremental development and degradation issues. This includes the requirement for the ongoing participation of key lake basin management institutions and their operations. This study’s objective is to assess the impacts of land use activities along the Kenyir Lake Basin based on the sedimentation problem level. There are a few hydrological methods that are necessary indicators to measure the level of sediment production, such as Total Suspended Solid (TSS), area of sub-catchment, river discharge measurement, and annual sediment load production. The results showed that the sub-catchment of Besar River released the lowest annual average estimation at 3833.70 kg/km2/year, and the sub-catchment of Kenyir River produced the highest annual average estimation at 128,070.86 kg/km2/year for annual sediment load flow produced from tributary rivers into Kenyir Lake. Kenyir Lake Basin’s downstream and midstream regions had higher sediment load values than its upstream regions. This study highlighted the significance of the effects of anthropogenic factors, hydrological, geomorphological, growth, and developmental factors, and climate changes as the key variables attributing to the sedimentation phenomenon along the Kenyir Lake Basin. The construction of a long-term lake or reservoir catchment development and management plan, combined with the formation of a vision and comprehensive strategic plan, are vital components of sound management practice. The efficient implementation of the suggested watershed management programmes depends on the active involvement of all significant catchment stakeholders.

show abstract

Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction

Cited by 26 publications

References 54 publications

Nonlinear response modelling of material systems using constrained Gaussian processes

Nonlinear response modelling of material systems using constrained Gaussian processes

Application of Machine Learning Models to Bridge Afflux Estimation

The Assessment of Sedimentation Problems in Kenyir Hydropower Reservoir, Malaysia

Contact Info

Product

Resources

About