Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods

Kişi, Özgür; Azad, Armin; Kashi, Hamed; Saeedian, Amir; Hashemi, Seyed Ali Asghar; Ghorbani, Salar

doi:10.1007/s11269-018-2147-6

Cited by 72 publications

(27 citation statements)

References 23 publications

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“…4) was chosen as the best model to predict the tunnel performance, as shown in Table 6. In this study, the convergence speed of proposed model is considered [16]. Results showed that the 300 iterations required about 20 minutes to train the system.…”

Section: B Improved Pso-anfis Modelmentioning

confidence: 99%

Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS

et al. 2020

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This paper proposes a new computational model to predict the earth pressure balance (EPB) shield performance during tunnelling. The proposed model integrates an improved particle swarm optimization (PSO) with adaptive neurofuzzy inference system (ANFIS) based on the fuzzy C-mean (FCM) clustering method. In particular, the proposed model uses shield operational parameters as inputs and computes the advance rate as the output. Prior to modeling, critical operational parameters are identified through principle component analysis (PCA). The hybrid model is applied to the prediction of the shield performance in the tunnel section of Guangzhou Metro Line 9 in China. The prediction results indicate that the improved PSO-ANFIS model shows high accuracy in predicting the EPB shield performance in terms of the multiobjective fitness function [i.e. root mean square error (RMSE) = 0.07, coefficient of determination (R 2) = 0.88, variance account (VA) = 0.84 for testing datasets, respectively]. The good agreement between the actual measurements and predicted values demonstrates that the proposed model is promising for predicting the EPB shield tunnel performance with good accuracy.

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Section: B Improved Pso-anfis Modelmentioning

confidence: 99%

Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS

et al. 2020

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“…Every single particle approximates its situation relative to the target position. They finetune their location and velocity using the current situation and the best position they were already in and the situation of the best particles in the bunch [57]:…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

et al. 2020

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Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station 1 ) and Mosul (Station 2 ) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r = 0.971 during testing phase at Station 1 and Station 2 , respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.INDEX TERMS Wind speed prediction, multivariate empirical mode decomposition, random forest, Kernel Ridge Regression, Iraq region.

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“…This study revealed the competency of metaheuristic-based learning for spatial assessment of landslide, which is a frequent devastating environmental hazard. The ANFIS is a leading notion of machine learning techniques that have been successfully applied to various issues, including groundwater modeling (Chen, Panahi, et al 2019;Kisi et al 2019), treatments of natural gas storage , and forest fire hazard assessment (Jaafari et al 2019). But there are some drawbacks of this tool for high dimensional problems which (according to the findings of earlier studies) require the assist of metaheuristic techniques for overcoming them.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Spatial assessment of landslide risk using two novel integrations of neuro-fuzzy system and metaheuristic approaches; Ardabil Province, Iran

Moayedi

Khari

Bahiraei

et al. 2020

Geomatics, Natural Hazards and Risk

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This article addresses the spatial analysis of landslide susceptibility in the Ardabil province of Iran. To this end, two well-known optimization algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO) are synthesized with an adaptive neuro-fuzzy inference system (ANFIS) to create the ensembles of GA-ANFIS and PSO-ANFIS. Besides, the statistical index (SI) model is also performed to be compared with the mentioned intelligent techniques. Fourteen landslide conditioning factors including elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI), and lithology were considered within the geographic information system (GIS). Out of 253 identified landslides, 177 points (70% of them) were randomly selected and used for the training phase, and the remaining 76 points (30% of them) were used to evaluate the accuracy of the SI, GA-ANFIS, and PSO-ANFIS models. Referring to the calculated area under the receiver operating characteristic curve (AUROC) index, the GA-ANFIS (AUROC ¼ 0.914) and SI (AUROC ¼ 0.821) showed the best performance, respectively in the training and testing phases. Notably, ANFIS-PSO emerged as the faster prediction method compared to the GA-ANFIS. Also, from spatial analysis, it was revealed that around 95%, 87%, and 97% of the training landslides, and 96%, 84%, and 76% of the testing landslides are located in hazardous areas.

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Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods

Cited by 72 publications

References 23 publications

Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS

Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

Spatial assessment of landslide risk using two novel integrations of neuro-fuzzy system and metaheuristic approaches; Ardabil Province, Iran

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