Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows

Ghorbani, Mohammad Ali; Khatibi, Rahman; Karimi, Vahid; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Zounemat‐Kermani, Mohammad

doi:10.1007/s11269-018-2038-x

Cited by 68 publications

(18 citation statements)

References 28 publications

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“…It could be said that, currently, a model that outperforms other models in various hydrological conditions is non-existent. It may not be feasible to generate consistent prediction using several models due to the dynamic nature and non-stationarity of historical data; hence, studies are needed to develop more efficient models based on the available historical data [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation

et al. 2020

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Streamflow forecasting is essential for hydrological engineering. In accordance with the advancement of computer aids in this field, various machine learning (ML) models have been explored to solve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly explored version of an ML model called the long short-term memory (LSTM) was investigated for streamflow prediction using historical data for forecasting for a particular period. For a case study located in a tropical environment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. The modelling was performed according to several perspectives: (i) The feasibility of applying the developed LSTM model to streamflow prediction was verified, and the performance of the developed LSTM model was compared with the classic backpropagation neural network model; (ii) In the experimental process of applying the LSTM model to the prediction of streamflow, the influence of the training set size on the performance of the developed LSTM model was tested; (iii) The effect of the time interval between the training set and the testing set on the performance of the developed LSTM model was tested; (iv) The effect of the time span of the prediction data on the performance of the developed LSTM model was tested. The experimental data show that not only does the developed LSTM model have obvious advantages in processing steady streamflow data in the dry season but it also shows good ability to capture data features in the rapidly fluctuant streamflow data in the rainy season. INDEX TERMS Deep learning model, streamflow forecasting, tropical environment, window scale forecasting, LSTM.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation

et al. 2020

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“…We assessed and compared the predction performance of our proposed hybrid model SSA-VMD-EBT-SVM with other existing models (EMD-SVM, EEMD-SVM, VMD-SVM, SSA-EMD-SVM, SSA-EEMD-SVM, SSA-VMD-SVM) as a benchmark using following four measures: the Nash-Sutcliffe Efficiency (NSE), Mean Square Error (MSE), Root Mean Square Error (RMSE) (Ghorbani et al, 2018) and Mean Absolute Error (MAE) (Yaseen et al, 2018) with following equations respectively; where y ot is the observed values, is the mean of observed values and y pt is predicted value of model. Moreover, Taylor diagram is used to prepare a visual comprehension with the help of polar plot for the evaluation of modeling results.…”

Section: Proposed Methodologymentioning

confidence: 99%

Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model

Nazir

Hussain

Faisal

et al. 2019

PeerJ

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River inflow prediction plays an important role in water resources management and power-generating systems. But the noises and multi-scale nature of river inflow data adds an extra layer of complexity towards accurate predictive model. To overcome this issue, we proposed a hybrid model, Variational Mode Decomposition (VMD), based on a singular spectrum analysis (SSA) denoising technique. First, SSA his applied to denoise the river inflow data. Second, VMD, a signal processing technique, is employed to decompose the denoised river inflow data into multiple intrinsic mode functions (IMFs), each with a relative frequency scale. Third, Empirical Bayes Threshold (EBT) is applied on non-linear IMF to smooth out. Fourth, predicted models of denoised and decomposed IMFs are established by learning the feature values of the Support Vector Machine (SVM). Finally, the ensemble predicted results are formulated by adding the predicted IMFs. The proposed model is demonstrated using daily river inflow data from four river stations of the Indus River Basin (IRB) system, which is the largest water system in Pakistan. To fully illustrate the superiority of our proposed approach, the SSA-VMD-EBT-SVM hybrid model was compared with SSA-VMD-SVM, VMD-SVM, Empirical Mode Decomposition (EMD) based i.e., EMD-SVM, SSA-EMD-SVM, Ensemble EMD (EEMD) based i.e., EEMD-SVM and SSA-EEMD-SVM. We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Therefore, SSA-VMD-EBT-SVM model can be used for water resources management and power-generating systems using non-linear time series data.

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“…Due to their excellent accuracy, these methods are commonly used by experts in modeling hydrological processes such as linear and multilinear regression (LMR) (Abdulelah Al-Sudani et al, 2019; A. Ahani, Shourian, & Rahimi Rad, 2018), autoregressive models (AR) (Banihabib et al, 2019), genetic algorithm (GA) combination models (Yaghoubi et al, 2019), gene expression programming (GEP) (Das et al, 2019), artificial neural networks (ANNs) (Ghose & Samantaray, 2019;Xu et al, 2009), wavelet transform (WT) (Freire et al, 2019;Honorato et al, 2019;Ravansalar et al, 2017), adaptive neuro-fuzzy inference system (ANFIS) (Chang et al, 2019;Yaseen et al, 2017), bayesian neural network (BNN) (Ren et al, 2018), recurrent neural networks (RNNs) (Tian et al, 2018), support vector regression (SVR) Yu et al, 2020) support vector machine (Ghorbani et al, 2018). Sabzi et al conducted monthly streamflow modeling utilizing ANFIS, the standalone models of ANN and autoregressive integrated moving average (ARIMA), and an integrated ANN-ARIMA model by using snow telemetry data in Elephant Butte reservoir at Mexico city (Zamani Sabzi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling

Wang

Attar

Khalili

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows

Cited by 68 publications

References 28 publications

Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation

Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation

Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model

Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling

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