Suspended sediment discharge modeling during flood events using two different artificial neural network algorithms

Bouguerra, Hamza; Tachi, Salah Eddine; Derdous, Oussama; Bouanani, Abderrazak; Khanchoul, Kamel

doi:10.1007/s11600-019-00373-4

Cited by 18 publications

(6 citation statements)

References 33 publications

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“…To manage the complex river systems, it is crucial to accurately predict both instream discharges and sediment transport in an intricate network of channels, tributaries, and frequent flood occurrences. In this regard, there have been a few studies conducted to predict the sediment transport based on data-driven models for prediction based on ML (Adib and Mahmoodi 2017;Bouguerra et al 2019;Cigizoglu 2004;Cigizoglu and Alp 2006;Gupta et al 2021). A study by Adib and Mahmoodi (2017) investigated suspended sediment load changes in the Marun River, Iran, over 42 years due to urban development, deforestation, and population growth.…”

Section: Experimental Modelingmentioning

confidence: 99%

“…Using a perceptron artificial neural network (ANN) optimized by the Genetic Algorithm, the study predicted a suspended sediment load from 400,000 to 800,000 tons per day in future flood conditions (Sirdari et al 2014). Also, Bouguerra et al (2019) utilized ANN to predict suspended sediment discharges during floods in two Algerian catchments, Ressoul and Mellah, over 31 and 28 years, respectively. Two training algorithms, Levenberg-Marquardt (LM) and Quasi-Newton (QN) were compared, with QN demonstrating superior performance.…”

Section: Experimental Modelingmentioning

confidence: 99%

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A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Mir,

Patel

2024

JWMM

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The hydrological channel networks of the steep mountains are extensively designated and organized geographic systems and are very complicated. They are composed of granular beds that are uneven and are subjected to fluid forces that fluctuate spatially and temporally. The flow and movement of sediment in these streams are significantly shaped by large rocks such as boulders. However, it is difficult to comprehend these mountain streams because significant information is unavailable regarding these channels as compared to plane bed streams. To address this issue, a critical review of the numerical, computational fluid dynamics, and machine learning mechanisms underlying sediment transport across different flow conditions in steep channels is presented while considering the current and foreseeable conditions for various sediment transport phenomena. The present study emphasized to carry out further analysis on these steep channels using advanced available techniques to get an insight into the morphology of these channels. Furthermore, the hazards associated with steep mountain channels are reviewed as they have a significant impact on infrastructure and habitation in mountainous regions.

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Section: Experimental Modelingmentioning

confidence: 99%

Section: Experimental Modelingmentioning

confidence: 99%

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Mir,

Patel

2024

JWMM

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“…In recent decades, researchers have sought to use artificial intelligence approaches for modeling the rivers SSL. In other words, the focus of prediction models has changed from linear regression to artificial intelligence models, which can be referred to the studies of researchers such as: Ezzaouini et al (2022), Abda et al (2021, Tachi et al (2020), Bouguerra et al (2019), Buyukyildiz & Kumcu (2017), Chen andChau (2016), Olyaie et al (2015), Kim et al (2012), Wu et al (2010), Cimen (2008) and Van Kessel and Blom (1998).…”

Section: Introductionmentioning

confidence: 99%

Modeling daily suspended sediment load of rivers using artificial intelligence approaches

Shabani

2022

Preprint

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Knowing the amount of suspended sediment loda (SSL) carried by rivers is an important factor in watershed management. Hence, it is necessary to measure or estimate its amount accurately. In this paper, it was attempted to apply three artificial intelligence approaches including artificial neural network (ANN), artificial neural network combined with particle swarm optimization (ANN-PSO) and long short-term memory (LSTM) to predict the daily SSL, using the data of Jamal-Beig hydrometric station in Kharestan watershed, Fars province, Iran. To achieve this goal, the daily data of SSL (Qs) and flow (Qf) were collected during 30 years (1992-2020). Eighty percent of data was considered for the training phase and 20% for the validation phase. The performance of the models was evaluated using the RMSE, R, NSE and PBIAS criteria according to the estimated and measured SSL values. The results showed that the ANN-PSO model with lower values of PBIAS = -1.048% and RMSE = 26.494 ton/day and high values of NSE = 0.827 and R = 0.912 provides the best performance compared to ANN and LSTM models for estimating daily SSL in Kharestan watershed.

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“…An extensive review of ANN applications in the hydrological field for the estimation and prediction of numerous hydrological parameters has been acknowledged by the ASCE Task Committee [30,31]. In the last two decades, studies have shown that artificial neural networks (ANNs) have promising results in terms of modelling and forecasting streamflow [32,33], reservoir water level [34,35], and suspended sediment in rivers [2,[36][37][38]. The ANN model is especially employed where basic physical interactions are not entirely known, but there are sufficient data to train a network.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

2021

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A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirna and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R2 of 0.92 and a PBIAS of −0.59%. Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal.

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Suspended sediment discharge modeling during flood events using two different artificial neural network algorithms

Cited by 18 publications

References 33 publications

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Modeling daily suspended sediment load of rivers using artificial intelligence approaches

Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

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