Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques

Singh, Vijay Kumar; Kashyap, P. S.; Kisi, Özgür

doi:10.1007/s12665-018-7892-6

Cited by 25 publications

(10 citation statements)

References 53 publications

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“…PARE can be used to judge the model's under-prediction and over-prediction performance [23]. PARE's positive value indicates over-prediction, and PARE's negative values indicate the under-prediction performance of the developed model.…”

Section: Hydrological Indices Coefficient Of Efficiency (Ce)mentioning

confidence: 99%

“…AI-based data-driven models such as ANN do not need site-specific information that is rarely available but instead focuses on forming the relationship between input and output variables [20][21][22]. The modeling of different hydrological processes using ANN is the latest technique [23]. It considers both linearity and non-linearity concepts for model construction.…”

Section: Introductionmentioning

confidence: 99%

“…However, the fuzzy logic approach's main problem is that no systematic procedure is available to design a fuzzy controller [12]. The ANFIS technique has recently been applied successfully for rainfall-runoff-sediment modeling [7,21,23]. Generally, Fourier transform requires static data for investigation purposes, but signals of hydrological time series are highly non-stationary.…”

Section: Introductionmentioning

confidence: 99%

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Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Bajirao

Kumar

et al. 2021

Sustainability

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Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

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Section: Hydrological Indices Coefficient Of Efficiency (Ce)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Bajirao

Kumar

et al. 2021

Sustainability

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“…The gamma test is developed a non-linear relation between information (input) and yield (output) vector based on the minimum mean squared error in data [40]. The selection of relevant information and yield vector combination is the very crucial stage in any modeling because irrelevant information vector creates a large amount of uncertainty in the model [20]. Therefore, in the present research work, we are applied GT to find an appropriate relation between yield vector i.e., drought-tolerant index (DTI) and stress-tolerant index (STI), and information vector i.e., day of anthesis, day of silking, day to scensence, plant height, ear length, number of kernel and gross yield per plant.…”

Section: Gamma Test (Gt)mentioning

confidence: 99%

Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

Singh¹,

Kumar²,

Saran³

et al. 2022

Preprint

Self Cite

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Maize (Zea mays subsp. mays) is the staple food crop in the world. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of developed models was evaluated based on statistical indices and graphical representation. Results of gamma test based on the least value of gamma and standard error indices show that day of anthesis (DOA), day of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined as efficient information vector combination for drought-tolerant index (DTI) as well as the stress-tolerant index (STI). The results of MLP, SVM, MLP-GA, and SVM-GA algorithms were compared based on statistical indices and visual interpretation that have satisfactory for prediction of the drought-tolerant index and stress-tolerant index in maize crop. It has also seemed that genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found a better prediction of the drought-tolerant index and stress-tolerant index in maize crop. Similarly, the SVM-GA model has the highest potential to forecast the DTI and STI in maize crops as compared to MLP, SVM, MLP-GA models.

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“…Olyaie et al [41] compared the performance of various conventional sediment-rating curves (SRCs) and soft-computing approaches such as ANFIS and ANNs to estimate the daily SSL. Singh et al [42] modelled suspended sediment using four different heuristic techniques and two regression-based techniques. Pektas et al [43] investigated the extrapolation performance of the ANN models for suspended sediment data and concluded that the ANN model provides a closer estimation of the observed peaks than conventional models.…”

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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Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques

Cited by 25 publications

References 53 publications

Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Developing a Novel Hybrid Model for Prediction of Drought and Stress-Tolerant Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

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

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