Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India

Khan, Mohd Yawar Ali; Tian, Fuqiang; Hasan, Fazil; Chakrapani, G. J.

doi:10.1016/j.ijsrc.2018.09.001

Cited by 77 publications

(14 citation statements)

References 57 publications

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“…The river enters in the Ganga Flood Plains (GFP) at Nanakmatta town, where the famous Nanaksagar dam has been built. After the confluence of the Ramganga, another major tributary of the Ganges, the river is connected to the Ganges River in the Kannauj area of Uttar Pradesh [36][37][38][39]. The average elevation of the Garra river is 530 m above sea level (a.s.l) with a total catchment area of 6,832 km 2 .…”

Section: Study Areamentioning

confidence: 99%

Evaluation of physicochemical and heavy metals characteristics in surface water under anthropogenic activities using multivariate statistical methods, Garra River, Ganges Basin, India

Khan

Wen

2020

Environmental Engineering Research

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Water samples from twenty-six locations were collected during March 2014, to evaluate the spatial variation in the water quality of Garra River, Ganges Basin. The physicochemical and heavy metals concentration along the river water were significantly different between the natural and urbanized areas. In the case of the mainstream, the chemical oxygen demand (COD) and the biological oxygen demand (BOD 5), which indicate the levels of organic pollution of the river water, were in the range of 8.1-51.4 mg/L and 5.8-26.9 mg/L, respectively. On the other hand, in the case of tributaries, these values were in the range of 19-36 mg/L and 11.2-20 mg/L, respectively. Multivariate statistical analysis was also carried out to discriminate sources of spatial variation of water quality. The results of principal component analysis (PCA) show the relationship between physicochemical parameters suggesting that the quality of water is mostly controlled by carbonate weathering. Dolomite is most likely to be present, which is a common source of Ca + and Mg +. Spatial distribution of PO 4 3with a high concentration in the downstream area of river (average 0.35 mg/L) suggests runoff from agricultural fields in this region as the main source.

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Section: Study Areamentioning

confidence: 99%

Evaluation of physicochemical and heavy metals characteristics in surface water under anthropogenic activities using multivariate statistical methods, Garra River, Ganges Basin, India

Khan

Wen

2020

Environmental Engineering Research

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“…Another study investigated the ability of the ANN method for modeling sophisticated nonlinear SSL in the Himalayan area [25]. The research not only presented an understanding of the sedimentation process, but it also studied the impact of other hydrological variables on SSL.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions

et al. 2019

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Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS–BA, ANFIS–WA, MFNN–BA, and MFNN–WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash–Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS–BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0. 75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS–BA had more reliable performance compared to other models. Thus, the ANFIS–BA model has high potential for predicting SSL.

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“…These phenomena attract researcher attention to develop direct and also indirect simulation and prediction models that could be accepted by operators worldwide; however, there is a demand to look into each catchment for better forecasting (Abrahart et al 2008 andKhan et al 2019). The estimation of suspended sediment is challenging because it is closely related to flow and the mechanism of their non-linear relationship and their complex interactions with each other.…”

Section: Introductionmentioning

confidence: 99%

A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

Hayder¹,

Solihin²,

Kushiar³

2021

J. Ecol. Eng.

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Sediment is a universal issue that is generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful AI approach. The data was collected from eight locations in upstream area of Ringlet reservoir catchment. The input variables are discharge and suspended solid. It was found that there is strong correlation between sediment and suspended solid with correlation coefficient of R = 0.9. The developed ML model successfully estimated the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result was produced by SVM (v-SVM version) where very low RMSE was generated for both training and testing dataset despite its more complicated hyperparameters setup. The results also show a promising application of machine learning for future prediction in hydro-informatic systems.

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Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India

Cited by 77 publications

References 57 publications

Evaluation of physicochemical and heavy metals characteristics in surface water under anthropogenic activities using multivariate statistical methods, Garra River, Ganges Basin, India

Evaluation of physicochemical and heavy metals characteristics in surface water under anthropogenic activities using multivariate statistical methods, Garra River, Ganges Basin, India

Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions

A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

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