Journal Article10.1007/S00521-020-05458-6
Artificial intelligence simulation of suspended sediment load with different membership functions of ANFIS
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TL;DR: The results showed that the ANFIS model can provide fast computational calculation, and adding more nodes for the prediction cannot change the overall time of calculation due to the meshless behavior of the model.
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Abstract: Modeling suspended sediment load is a critical element of water resources engineering. In this work, using the ANFIS method, everyday suspended sediment particles were estimated in different categories of the river in US Sediment big data, and various flow rates were utilized for testing and training. The artificial intelligent (AI) method called ANFIS is used to train actual data from the river and provide an AI model with artificial data points. This artificial data point can show the occurrence of disaster for a critical day with different flow rates. The changing parameter in the AI model enables us to make a correct decision about critical time for rivers. This study also concentrates on the sensitivity investigation of ANFIS setting parameters on the accurateness of numerical results in order to find the best ANFIS model for rapid oscillation in the data set. The best performance of the ANFIS method is achieved with the trimf membership function, the number of input membership function = 16, and the number of iteration = 1000. The results also showed that the ANFIS model can provide fast computational calculation, and adding more nodes for the prediction cannot change the overall time of calculation due to the meshless behavior of the model. In addition to this model, we used the ant colony method for training of data set, and we found that the ANFIS method is better in learning and prediction of the dataset.
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Citations
Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils
Işık Yilmaz,Oğuz Kaynar +1 more
- 01 May 2010
TL;DR: It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%.
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Artificial intelligence for suspended sediment load prediction: a review
TL;DR: In this paper, the authors summarize various existing artificial intelligence (AI)-based sediment load estimation models to calculate the suspended sediment load, to the best of our knowledge, and describe a few popular AI-based models that have been used for sediment load prediction.
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Utilizing Artificial Intelligence to Predict the Superplasticizer Demand of Self-Consolidating Concrete Incorporating Pumice, Slag, and Fly Ash Powders.
TL;DR: In this paper, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design.
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Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
TL;DR: In this article , the authors proposed a single model that is capable of accurately predicting suspended sediment load (SSL) for any river data set within Peninsular Malaysia, based on support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms.
Machine learning based simulation of an anti-cancer drug (busulfan) solubility in supercritical carbon dioxide: ANFIS model and experimental validation
TL;DR: The model developed in this work indicated that the ANFIS model can be used to accurately predict the solubility of drugs in supercritical solvents which can be consequently used for production of drugs with improved efficacy.
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References
Flow visualization and analysis of thermal distribution for the nanofluid by the integration of fuzzy c-means clustering ANFIS structure and CFD methods
TL;DR: Using ANFIS method, it is possible to reduce the computation time of CFD method so that more nodes are predicted in a shorter period of time, while clustering method can enhance the computing time for each neural cell.
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Sediment Rating Curves for a Clearcut Ponderosa Pine Watershed in Northern Arizona
TL;DR: In this paper, the relationship between suspended sediment concentration and streamflow discharge was identified by geometric least-squares regression using 515 paired suspended sediment measurements obtained from 1974 through 1982.
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Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants
Shahab Shamshirband,Meisam Babanezhad,Amir Mosavi,Amir Mosavi,Amir Mosavi,Narjes Nabipour,Éva Hajnal,László Nádai,Kwok Wing Chau +8 more
TL;DR: The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.
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Visualization of nanofluid flow field by adaptive-network-based fuzzy inference system (ANFIS) with cubic interpolation particle approach
TL;DR: The results show that the clustering framework can visualize the flow pattern in the square-shaped cavity in a short time and the combination of CFD and smart modeling enables us to specifically analyze and visualize one part of a fluid structure without several complex CFD analyses.
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Prediction of Flow Characteristics in the Bubble Column Reactor by the Artificial Pheromone-Based Communication of Biological Ants
Shahab Shamshirband,Meisam Babanezhad,Amir Mosavi,Amir Mosavi,Amir Mosavi,Narjes Nabipour,Éva Hajnal,László Nádai,Kwok Wing Chau +8 more
- 11 Jan 2020
TL;DR: A novel combination of the ant colony optimization algorithm (ACO) and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors.