Inflow forecasting using Artificial Neural Networks for reservoir operation

Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

doi:10.5194/piahs-373-209-2016

Cited by 19 publications

(8 citation statements)

References 6 publications

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“…Luk et al, [50], have applied the three types of ANNs, namely Multilayer Feed forward Neural Networks, Partial Recurrent Neural Networks and Time Delay Neural Networks for prediction of Rainfall forecasting and found that the ANN gives better result than other models. Khalili et al, [40], have utilized ANN (Artificial Neural Network) modeling with a new approach like three-layer feed-forward perceptron network with back propagation algorithm to predict daily rainfall forecasting. Further they have also described about hidden dynamics of rainfall through the past information of the system and concluded that the used model is best for daily rainfall forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Yadu¹,

Shrivastava²

2019

IJCET

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India is the agrarian country. The overall economy of our country is based on agriculture. Although the methods of cultivation are traditional and not hi-tech thus more over 75% of our farmers are dependent on monsoon. Prediction of actual monsoon is a challenge for meteorological scientists. Since the climatic data time series shows highly non-linear and chaotic behavior thus its forecast is still an enigma. Thus, forecasting of climate phenomenon is a challenging issue for the researchers round the globe. However, it is a prime necessity to forecast climatic changes such as Rainfall (daily rainfall, monthly rainfall, heavy rainfall etc.), Flood, Drought, minimum and maximum Temperature, River flow etc. To recognize applications of Artificial Neural Network (ANNs) in weather forecasting, especially in drought forecasting a comprehensive literature review from 2000 to 2017 is done and presented in this paper. In the study, more over 90 contributions have been surveyed and it has been observed that the architecture of ANN such as BPN, RBFN, MLP, ANFIS, ARIMA etc. are found best to forecast chaotic behavior and have efficient enough to forecast drought as well as other weather phenomenon over broader or smaller homogeneous region.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Yadu¹,

Shrivastava²

2019

IJCET

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“…Many studies have applied data-driven models to water-quality simulations, runoff forecasting, water level forecasting, wind speed forecasting, etc. [19][20][21][22][23]. Maniquiz et al [24] used multiple linear regression (MLR) to establish an equation for estimating pollutant load with rainfall as a variable, indicating that total rainfall and average rainfall intensity can be used as predictors of pollutant load.…”

Section: Introductionmentioning

confidence: 99%

Research on Runoff Simulations Using Deep-Learning Methods

et al. 2021

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Runoff simulations are of great significance to the planning management of water resources. Here, we discussed the influence of the model component, model parameters and model input on runoff modeling, taking Hanjiang River Basin as the research area. Convolution kernel and attention mechanism were introduced into an LSTM network, and a new data-driven model Conv-TALSTM was developed. The model parameters were analyzed based on the Conv-TALSTM, and the results suggested that the optimal parameters were greatly affected by the correlation between the input data and output data. We compared the performance of Conv-TALSTM and variant models (TALSTM, Conv-LSTM, LSTM), and found that Conv-TALSTM can reproduce high flow more accurately. Moreover, the results were comparable when the model was trained with meteorological or hydrological variables, whereas the peak values with hydrological data were closer to the observations. When the two datasets were combined, the performance of the model was better. Additionally, Conv-TALSTM was also compared with an ANN (artificial neural network) and Wetspa (a distributed model for Water and Energy Transfer between Soil, Plants and Atmosphere), which verified the advantages of Conv-TALSTM in peak simulations. This study provides a direction for improving the accuracy, simplifying model structure and shortening calculation time in runoff simulations.

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“…e SVM [11,12] and the SVR [13,14] have been used in the field of streamflow forecasting research. It is important to remark that the artificial neural networks (ANN) represent the most widely applied artificial intelligence techniques for modelling [15][16][17], and it has been widely used in hydrology [18][19][20]. e backpropagation neural network (BPNN) is the improvement of the ANN learning representations by error backpropagation algorithm, which is the most popular neural network.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

Zhang

2020

Complexity

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Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition (VMD) and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. The ensemble forecast was finally reconstructed by summing. Historical daily streamflow series recorded at the Wushan and Weijiabao hydrologic stations from 1 January 2001 to 31 December 2014 in China were investigated using the proposed VMD-based models. Three quantitative evaluation indexes, including the Nash–Sutcliffe efficiency coefficient (NSE), the root mean square error (RMSE), and the mean absolute error (MAE), were used to evaluate and compare the predicted results of the proposed VMD-based models with two other models such as nondecomposition method (BPNN) and BPNN based on ensemble empirical mode decomposition (EEMD-BPNN). Furthermore, a comparative analysis of the performance of the VMD-BPNN model under different forecast periods (1, 3, 5, and 7 days) was performed. The results evidenced that the proposed VMD-based models could always achieve good performance in the testing stage and had relatively good stability and representativeness. Specifically, the VMD-BPNN model considered both the prediction accuracy and computation efficiency. The results show that the reliability of the forecasting decreased as the foresight period increased. The model performed satisfactorily up to 7-d lead time. The VMD-BPNN model could be applied as a promising, reliable, and robust prediction tool for short-term streamflow forecasting modelling.

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Inflow forecasting using Artificial Neural Networks for reservoir operation

Cited by 19 publications

References 6 publications

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Research on Runoff Simulations Using Deep-Learning Methods

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

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