Groundwater level fluctuations simulation and prediction by ANFIS- and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: Application to the Miandarband plain

Zare, Mohammad; Koch, Manfred

doi:10.1016/j.jher.2017.11.004

Cited by 96 publications

(39 citation statements)

References 31 publications

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“…Fuzzy logic and ANN combine to form the ANFIS model, which is known as a multi-layer feed forward network. A first order of the Sugeno fuzzy model included the following equations [31]: (Figure 2). Layer 1: the fuzzy membership function (MF) generates the fuzzy membership grads for the nodes.…”

Section: Anfismentioning

confidence: 99%

“…The best selections for the shape factions in the previous study were the normalized Gaussian and bell-shaped MF. The Gaussian MF for this study was selected because it is smooth and non-zero for all points [31]:…”

Section: Anfismentioning

confidence: 99%

“…where A 1 , B 1 , B 2 and B 2 are membership functions, x and y are inputs, f 1 and f 2 are output functions and p 1 , q 1 , r 1 , p 2 , q 2 and r 2 are linear parameters(Figure 2).Fuzzy logic and ANN combine to form the ANFIS model, which is known as a multi-layer feed forward network. A first order of the Sugeno fuzzy model included the following equations[31]:…”

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confidence: 99%

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Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

Ehteram

Afan

Dianatikhah

et al. 2019

Water

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The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.

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Section: Anfismentioning

confidence: 99%

Section: Anfismentioning

confidence: 99%

mentioning

confidence: 99%

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Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

Ehteram

Afan

Dianatikhah

et al. 2019

Water

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“…This study utilizes the FCM clustering algorithm [22] to obtain the most representative samples for each class, and the initial rules are heuristically generated. The clustering number for each operating state of the wind turbine is selected as Equation (5):…”

Section: Generation Of Initial State Rulesmentioning

confidence: 99%

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

2019

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Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining, representative initial rules are generated via the combination of a clustering algorithm and heuristic learning. Then, a multi-population quantum evolutionary algorithm is utilized to optimize the rule base. In the stage of fault identification, abnormal states are identified via a fuzzy rule-based classification system, and probabilistic fault sorting with critical variables is realized according to the fuzzy reasoning of state rules. Ten common sensor and actuator faults in 5 MW offshore WTs are taken to verify the feasibility and superiority of the proposed scheme. Experimental results demonstrate that the proposed method has higher identification accuracy than other identification methods and thus prove the feasibility of the proposed probabilistic fault analysis scheme. data mining technologies have made considerable progress [11]. Consequently, data-based methods have received increasing attention in the area of fault detection and location of WTs [12].In data-based research, substantial improvements have been proposed to improve the accuracy of fault identification. Chen et al. [13] proposed a fault prediction method for WTs using an adaptive neuro-fuzzy inference system with a priori knowledge. This method provides a fault instruction and allows the operator to determine the repair plan before the failure worsens. Hu et al. [14] proposed a fault diagnosis method that combines SVM and domain knowledge to improve the identification accuracy for faults in WTs. However, the aforementioned methods depend excessively on expert knowledge, and their identification results are simplistic. Wang et al. [15] proposed an FDI scheme based on a variable selection method using principal component analysis; in this scheme, the result of fault identification is determined by the signal contribution, but the method can only identify an abnormal signal rather than a fault type. Ruiz et al. [16] proposed a fault classification method for WTs that transforms the domain signals of WTs into two-dimensional matrices. Certain similar faults are merged for identification in the experiment, and the separation of similar faults is unwarrantable. Pashazadeh et al. [17] obtained the fault data of WTs from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) simulator and Simulink (in a MATLAB environment) and developed an FDI scheme on the basis of classifier fusion, in which four classifiers are implemented in parallel. However, this m...

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“…In recent years, AI techniques have been increasingly used to solve a large number of environmental and water engineering problems. These include evolutionary polynomial regression (EPR) [8,9], ANFIS [10,11], gene expression programming (GEP) [12,13], model tree (MT) [14,15], support vector machine (SVM) [16][17][18], and extreme learning machine (ELM) [8,19]. Various researches have also used AI approaches particularly for river flow forecasting [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Rezaie-Balf

Nowbandegani

Phillips

et al. 2019

Water

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Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

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Groundwater level fluctuations simulation and prediction by ANFIS- and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: Application to the Miandarband plain

Cited by 96 publications

References 31 publications

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

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