Deriving stage–discharge–sediment concentration relationships using fuzzy logic

Lohani, A. K.; Goel, N. K.; Bhatia, Karamjit

doi:10.1623/hysj.52.4.793

Cited by 87 publications

(45 citation statements)

References 38 publications

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“…Data-mining techniques in this context represent a range of multivariate data-analysis methods to establish relationships between SS concentration and a set of variables. For example, quantile regression forests (Francke et al, 2008;Zimmermann et al, 2012), fuzzy logic (Kisi, 2005;Lohani et al, 2007), M5 model trees (Onderka et al, 2012), artificial neural networks (Cobaner et al, 2009) in other studies, their study demonstrates that data-driven models containing hydro-meteorological data perform better in predicting SS concentrations compared to sediment rating curves.…”

Section: Multivariate Data-mining Techniquesmentioning

confidence: 82%

Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation

Vercruysse

Grabowski

Rickson

2017

Earth-Science Reviews

328

181

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Suspended sediment is a natural part of river systems and plays an essential role in structuring the landscape, creating ecological habitats and transporting nutrients. It is also a common management problem, where alterations to sediment quantity and quality negatively impact ecological communities, increase flood hazard and shorten the lifespan of infrastructure. To address these challenges and develop appropriate sustainable management strategies, we need a thorough understanding of sediment sources, pathways and transport dynamics and the drivers that underlie spatial and temporal variability in suspended sediment transport in rivers. However, research to date has not sufficiently addressed the temporal complexity of sediment transport processes, which is limiting our ability to disentangle the hydro-meteorological, catchment, channel and anthropogenic drivers of suspended sediment transport in rivers. This review critically evaluates previously published work on suspended sediment dynamics to demonstrate how the interpretation of sediment sources and pathways is influenced by the temporal scale and methodology of the study. To do this, the review (i) summarizes the main drivers of temporal variation in suspended sediment transport in rivers; (ii) critically reviews the common empirical approaches used to analyze and quantify sediment sources and loads, and their capacity to account for temporal variations; (iii) applies these findings to recent case studies to illustrate how method and timescale affect the interpretation of suspended sediment transport dynamics; and finally (iv) synthesizes the findings of the review into a set of guidelines for a multi-timescale approach to sediment regime characterization. By recognizing a priori that study design and temporal scale have an impact on the interpretation of SS dynamics and employing methods that address these issues, future research will be better able to identify the drivers of suspended sediment transport in rivers, improve sediment transport modelling, and propose effective, sustainable solutions to sediment management problems.

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Section: Multivariate Data-mining Techniquesmentioning

confidence: 82%

Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation

Vercruysse

Grabowski

Rickson

2017

Earth-Science Reviews

328

181

View full text Add to dashboard Cite

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“…The research showed that the fuzzy approach performed better under very high rainfall intensities over different slopes and over very steep slopes under various rainfall intensities. Lohani et al (2007) developed a fuzzy inference system to simulate the stage-dischargesediment concentration relationship in two gauging stations in the Narmada basin in India. Results of the mentioned study showed that the fuzzy method was capable to provide much better results than rating curve method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

Rajaee

Mirbagheri

Nourani

et al. 2009

Int. J. Environ. Sci. Technol.

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This study investigated the prediction of suspended sediment load in a gauging station in the USA by neuro-fuzzy, conjunction of wavelet analysis and neuro-fuzzy as well as conventional sediment rating curve models. In the proposed wavelet analysis and neuro-fuzzy model, observed time series of river discharge and suspended sediment load were decomposed at different scales by wavelet analysis. Then, total effective time series of discharge and suspended sediment load were imposed as inputs to the neuro-fuzzy model for prediction of suspended sediment load in one day ahead. Results showed that the wavelet analysis and neuro-fuzzy model performance was better in prediction rather than the neuro-fuzzy and sediment rating curve models. The wavelet analysis and neuro-fuzzy model produced reasonable predictions for the extreme values. Furthermore, the cumulative suspended sediment load estimated by this technique was closer to the actual data than the others one. Also, the model could be employed to simulate hysteresis phenomenon, while sediment rating curve method is incapable in this event.

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“…Comparative analysis of estimated discharge by TS fuzzy, back propagation ANN and conventional rating curves showed TS fuzzy as the most reliable approach among the three approaches. Lohani et al (2007) introduced the concept of developing stage-discharge-sediment concentration relationship using fuzzy logic. Considering the daily gauge and discharge data of the Jamtara and Manot gauging sites of river Narmada, India Lohani et al (2007) demonstrated the superiority of fuzzy logic based approach over conventional rating approach as well as ANN approach.…”

Section: Materials and Methodologymentioning

confidence: 99%

“…Lohani et al (2007) introduced the concept of developing stage-discharge-sediment concentration relationship using fuzzy logic. Considering the daily gauge and discharge data of the Jamtara and Manot gauging sites of river Narmada, India Lohani et al (2007) demonstrated the superiority of fuzzy logic based approach over conventional rating approach as well as ANN approach. Following a similar approach considering the data sets of two stations (Chester and Thebes) on river Mississippi and Conococheague creek in USA Jain (2008) investigated the generalisation capabilities of compound neural networks (CNN) by developing the integrated stage discharge suspended sediment rating curves.…”

Section: Materials and Methodologymentioning

confidence: 99%

Application of Data Driven Techniques in Discharge Rating Curve - An Overview

Zakwan

Muzzammil

Alam

2017

Aquademia: Water, Environment and Technology

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Establishing a reliable rating curve is an integral part of water resource engineering. The present paper is a review of data driven techniques used for modelling the stage discharge relationship over the last two decades. Over the year's several data driven techniques such as ANN, SVM, MT, fuzzy logic and GA have been applied by researchers in quest of arriving at a reliable technique to model the stage discharge relationship. Although, during this period several techniques have been used to establish the stage discharge relationship but the application of modern optimization tools such as ACO, PSO or PS has remained limited in this field. Therefore, application of modern optimization techniques and their comparison with existing literature would be appreciated in near future.

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Deriving stage–discharge–sediment concentration relationships using fuzzy logic

Cited by 87 publications

References 38 publications

Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation

Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation

Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

Application of Data Driven Techniques in Discharge Rating Curve - An Overview

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