Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction

Meshram, Sarita Gajbhiye; Singh, Vijay P.; Kisi, Özgür; Karimi, Vahid; Meshram, Chandrashekhar

doi:10.1007/s11269-020-02672-8

Cited by 61 publications

(23 citation statements)

References 65 publications

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“…The Artificial Intelligence (AI) technique has an advantage over traditional methods. It can perform well with a large amount of noisy data resulting from the dynamic and non-linear system where the system's fundamental physical relationships are unknown [14][15][16][17]. AI techniques can solve the complex problems of different hydrologic processes [18].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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 Radial Basis Function Neural Network (RBF) is a forward type network based on the radial basis function, which can approximate any finite function with arbitrary precision (Tayyab et al, 2018;She and You, 2019). Compared with other neural networks, RBF has the advantages of fast convergence, it does not easily fall into local minima, good robustness and easy implementation, and has been widely used in the field of nonlinear time series forecasting (Meshram et al, 2020).…”

Section: Radial Basis Function and Eemd-rbfmentioning

confidence: 99%

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

et al. 2021

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Hydrological series data are non-stationary and nonlinear. However, certain data-driven forecasting methods assume that streamflow series are stable, which contradicts reality and causes the simulated value to deviate from the observed one. Ensemble empirical mode decomposition (EEMD) was employed in this study to decompose runoff series into several stationary components and a trend. The long short-term memory (LSTM) model was used to build the prediction model for each sub-series. The model input set contained the historical flow series of the simulation station, its upstream hydrological station, and the historical meteorological element series. The final input of the LSTM model was selected by the MI method. To verify the effect of EEMD, this study used the Radial Basis Function (RBF) model to predict the sub-series, which was decomposed by EEMD. In addition, to study the simulation characteristics of the EEMD-LSTM model for different months of runoff, the GM(group by month)-EEMD-LSTM was set up for comparison. The key difference between the GM-EEMD-LSTM model and the EEMD-LSTM model is that the GM model must divide the runoff sequence on a monthly basis, followed by decomposition with EEMD and prediction with the LSTM model. The prediction results of the sub-series obtained by the LSTM and RBF exhibited better statistical performance than those of the original series, especially for the EEMD-LSTM. The overall GM-EEMD-LSTM model performance in low-water months was superior to that of the EEMD-LSTM model, but the simulation effect in the flood season was slightly lower than that of the EEMD-LSTM model. The simulation results of both models are significantly improved compared to those of the LSTM model.

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“…when |r| → ∞. ∅(r) → 0. σ is the spreading coefficient of the Gaussian function, which is defined experimentally (Meshram et al 2020). The model output is estimated from the following equation:…”

Section: Radial Basis Function Neural Network (Rbf)mentioning

confidence: 99%

On the Determination of Groundwater Level using Temporal and Spatial Parameters: Advanced Machine Learning Methods

Najafabadipour¹,

Kamali²,

Nezamabadi‐pour³

2021

Preprint

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Prediction of groundwater level is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and reduce high costs. For this purpose, four multilayer perceptron (MLP) neural network models and four cascade forward (CF) neural network models optimized with Bayesian Regularization (BR), Levenberg-Marquardt (LM), Resilient Backpropagation (RB), and Scaled Conjugate Gradient (SCG), as well as a radial basis function (RBF) neural network model and a generalized regression (GR) neural network model were developed to predict groundwater level using 1377 data point. This data set includes 12 spatial parameters divided into two categories of sediments and bedrock, and besides, 6 time series parameters have been used. Also, to determine the best models and combine them, 165 extra validation data points have been used. After identifying the best models from the three candidate models with lower average absolute relative error (AARE) value, the committee machine intelligence system (CMIS) model has been developed. The proposed CMIS model predicts groundwater level data with high accuracy with an AARE value of less than 0.11%. Also, the proposed model was compared with ten other models through graphical and statistical error analysis. The results show that the developed CMIS model performs better than other existing models in terms of precision and validity range. The relevancy factor indicates that the electrical resistivity of sediments had the highest effect on the groundwater level. Eventually, the quality of the data used was investigated both statistically and graphically, and the results show satisfactory reliability of the data used.

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Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction

Cited by 61 publications

References 65 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

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

On the Determination of Groundwater Level using Temporal and Spatial Parameters: Advanced Machine Learning Methods

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