Design of a Hybrid ANFIS–PSO Model to Estimate Sediment Transport in Open Channels

Ebtehaj, Isa; Bonakdari, Hossein; Es-haghi, Mohammad Sadegh

doi:10.1007/s40996-018-0218-9

Cited by 36 publications

(16 citation statements)

References 13 publications

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“…is is clearly an indication to the performance of the Particle Swarm Optimization algorithm for tuning the internal parameters of the ANFIS model and particularly for simulating the investigated geotechnical problem "i.e., shallow foundation settlement." It is worth to highlight that the ability of the PSO algorithm was approved for optimizing ANFIS model over multiple engineering applications such as channel sediment transport, basin bank shape optimization, compressive strength of intact roach, friction capacity ration of driven piles, oil flocculated asphaltene weight percentage, and several others [73][74][75][76][77].…”

Section: Application Analysis and Discussionmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

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Section: Application Analysis and Discussionmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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“…The two hybrid methods of ANFIS-GA and ANFIS-PSO are used to develop the prediction models of exergy destruction and energy consumption. Both proposed methods have recently been gained popularity for advancing prediction models in a wide range of engineering applications including the control systems [36][37][38][39]. The ANFIS-GA hybridizes the components of a single ANFIS and genetic algorithm (GA) [40].…”

Section: B Hybrid Machine Learning Methodsmentioning

confidence: 99%

Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

Ardabili

Beszedes

Nádai

et al. 2020

Preprint

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Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R 2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

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“…It was been shown that a Gaussian membership function led to accurate outputs (Azimi et al 2019;Ebtehaj et al 2019;Gholami et al 2019). Thus, the current study uses this membership function:…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Soft computing technique-based prediction of water quality index

Singh¹,

Sihag

Singh

et al. 2021

Water Supply

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Water quality (WQ) plays a crucial role in management of water resources. Water quality index (WQI) is frequently used methods to assess of water quality for drinking purposes. WQI can be predicted using chemical analysis which might not, however, be viable for a longer period in all the country-scale rivers. Thus, in this investigation, two neural-based soft computing techniques-artificial neural network (ANN), generalized neural network (GRNN)- and one hybrid soft computing techniques- adaptive neuro-fuzzy interference system (ANFIS) with four membership function- were used to predict WQI in Khorramabad, Biranshahr and Alashtar sub-watersheds in Iran. Ten distinct physiochemical parameters were used as input variables and WQI as output. Simultaneously, a correlation plot and pairs were used to ascertain the relation of input and output variables. The soft computing techniques were compared using six fitness criteria: NSE, MAE, LMI, RMSE, MAPE, CC. Results indicated that ANN better predicted WQI than did GRNN and ANFIS. Among the different membership functions of ANFIS, ANFIS_trimf was far better than were other membership functions. Thus, it was concluded that ANN was a viable tool for the prediction of WQI.

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Design of a Hybrid ANFIS–PSO Model to Estimate Sediment Transport in Open Channels

Cited by 36 publications

References 13 publications

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

Soft computing technique-based prediction of water quality index

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