A new approach to flow simulation using hybrid models

Solgi, Abazar; Zarei, Heidar; Nourani, Vahid; Bahmani, Ramin

doi:10.1007/s13201-016-0515-z

Cited by 19 publications

(7 citation statements)

References 23 publications

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“…The artificial neural network as an AI-based model is a mathematical model aiming to handle non-linear relationship of input-output data set [49]. ANN has proved to be effective with regards to complex function in various fields, including prediction, pattern recognition, classification, forecasting, control system and simulation [50,51]. Among the different ANN algorithms, FFNN with back propagation (BP) training is widely applied and is the most common class of ANNs.…”

Section: Feed Forward Neural Network (Ffnn) Conceptmentioning

confidence: 99%

“…The conjunction of ANN and fuzzy system presents a robust hybrid system which is capable of solving complex nature of the relationships [21,54]. ANFIS is a multi-layer feed-forward (MLFF) neural network that is capable of integrating the knowledge of ANN and fuzzy logic algorithms which maps the set of inputs to the outputs [51]. ANFIS as AI-based model employs the hybrid training algorithms which consist of a combination of BP and least squares method [55].…”

Section: Adaptive Neural Fuzzy Inference System (Anfis) Conceptmentioning

confidence: 99%

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Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps while in scenario 2, the central station’s data were imposed into the models, in addition to each station’s data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step.

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Section: Feed Forward Neural Network (Ffnn) Conceptmentioning

confidence: 99%

Section: Adaptive Neural Fuzzy Inference System (Anfis) Conceptmentioning

confidence: 99%

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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“…For this purpose, Eq. 1 was used as recommended by Solgi et al (2017). The input parameters of the model included precipitation, temperature, evaporation, and the GWL in a given month, and the output parameter was the GWL in the next month.…”

Section: Data Preparationmentioning

confidence: 99%

De-noising groundwater level modeling using data decomposition techniques in combination with artificial intelligence (case study Aspas aquifer)

2023

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Considering the recent significant drop in the groundwater level (GWL) in most of world regions, the importance of an accurate method to estimate GWL (in order to obtain a better insight into groundwater conditions) has been emphasized by researchers. In this study, artificial neural network (ANN) and support vector regression (SVR) models were initially employed to model the GWL of the Aspas aquifer. Secondly, in order to improve the accuracy of the models, two preprocessing tools, wavelet transform (WT) and complementary ensemble empirical mode decomposition (CEEMD), were combined with former methods which generated four hybrid models including W-ANN, W-SVR, CEEMD-ANN, and CEEMD-SVR. After these methods were implemented, models outcomes were obtained and analyzed. Finally, the results of each model were compared with the unit hydrograph of Aspas aquifer groundwater based on different statistical indexes to assess which modeling technique provides more accurate GWL estimation. The evaluation of the models results indicated that the ANN model outperformed the SVR model. Moreover, it was found that combining these two models with the preprocessing tools WT and CEEMD improved their performances. Coefficient of determination (R2) which indicates model accuracy was increased from 0.927 in the ANN model to 0.938 and 0.998 in the W-ANN and CEEMD-ANN models, respectively. It was also improved from 0.919 in the SVR model to 0.949 and 0.948 in the W-SVR and CEEMD-SVR models, respectively. According to these results, the hybrid CEEMD-ANN model is found to be the most accurate method to predict the GWL in aquifers, especially the Aspas aquifer.

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“…It is a difficult and challenging task for modelers to predict streamflow due to its complex (nonlinear) and non-deterministic characteristics. Hence, the execution of all simulation and predicting models is strongly based on the quality of the input data [11]. Data-Driven models (DDMs), which are a combination of two main groups called Artificial Intelligence (AI) and time-series methods, are popular because of the ability to compute, their influential theory, and their implicit process in modeling [12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

et al. 2020

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Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow.

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A new approach to flow simulation using hybrid models

Cited by 19 publications

References 23 publications

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

De-noising groundwater level modeling using data decomposition techniques in combination with artificial intelligence (case study Aspas aquifer)

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

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