Prediction of suspended sediment concentration from water quality variables

Bayram, Adem; Kankal, Murat; Tayfur, Gökmen; Önsoy, Hızır

doi:10.1007/s00521-012-1333-3

Cited by 26 publications

(5 citation statements)

References 24 publications

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“…They pointed out that the limited data set was one of the drawbacks of their research and encouraged others to collect more data to recalibrate and revalidate the model. Wang et al [19] employed a typical three-layer of MLP structure [77][78][79][80][81][82][83][84][85][86][87][88][89] with the BP algorithm to achieve Chl-a prediction. They divided the dataset into training (75%) and testing parts (25%).…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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“…Modelling the quantity of sediment load (SL) in rivers is vital for designing flow control and water storage facilities, for example, canals and dams 3 , 4 . In addition, suspended sediments impact drinking water quality supplies of residential localities and water requirements of industry and agriculture 5 , 6 . SSL is the outcome of various physical procedures, comprising detachment, transport, and settlement of particles that depend upon intensity and magnitude of rainfall, discharge in river network, land use, physical features of soil, and topography.…”

Section: Introductionmentioning

confidence: 99%

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Samantaray,

Sahoo,

Satapathy

et al. 2024

Sci Rep

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Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

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“…Direct analysis of the suspended sediment concentration (SSC) and the sediment rating curve (SRC) are among a wide range of tools used to observe the suspended sediment load. Although direct analysis is reliable, it is very costly and time consuming, and in many cases problematic for inaccessible sections, especially during severe storm events, and cannot be used for all river gauge stations (Bayram et al 2013). On the other hand, because SSC transport in a river is a complex hydrological phenomenon due to several parameters, such as the spatial variability of basin characteristics, river discharge patterns and the inherent nonlinearity of hydro-meteorological parameters, the conventional SRC method may not be suitable for estimating SSC (Joshi et al 2015); nor are regression models (RM), in which the system is assumed to be static (Ghorbani et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Suspended Sediment Concentration Modeling Using Conventional and Machine Learning Approaches in the Thames River, London Ontario

Mohamed

Shah

2018

JWMM

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Water resources management, hydraulic designs, environmental conservation, reservoir operation, river navigation and hydro-electric power generation all require reliable information and data about suspended sediment concentration (SSC). To predict such data, direct sampling and sediment rating curves (SRC) are commonly used. Direct sampling can be risky during extreme weather events and SRC may not provide satisfactory or dependable results, so engineers are developing new precise forecasting approaches. Various soft computing techniques have been used to model different hydrological and environmental problems, and have showed promising results. Prediction of SSC is a site-specific phenomenon and ought to be modeled for every river and creek. In this study, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models were compared with conventional SRC and linear regression methods. Using different combinations of observed SSC data and simultaneous stream discharge, water temperature, and electrical conductivity data for the Thames River at Byron Station, London Ontario from 1993 to 2016, several models were trained. Each model was evaluated using mean absolute error, root mean square error and the Nash-Sutcliffe efficiency coefficient. Results show that ANN models are more accurate than other modeling approaches for predicting SSC for this river.

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Prediction of suspended sediment concentration from water quality variables

Cited by 26 publications

References 24 publications

A Review of the Artificial Neural Network Models for Water Quality Prediction

A Review of the Artificial Neural Network Models for Water Quality Prediction

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Suspended Sediment Concentration Modeling Using Conventional and Machine Learning Approaches in the Thames River, London Ontario

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