List of Articles مدل ANFIS

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  1 - Modeling Daily River Flow Using Simulator Meta-Models (Case study: Gamasiab River)
  massoumeh zeinalie mohammad reza golabi mohammad hossein Niksokhan mohammad reza Sharifi
  10.22034/jest.2020.33087.4089
  Background and Aim: The aim is first to express the differences and identify three models, namely, Gene Expression Programming (GEP), Neural-Fuzzy Network (ANFIS), and Bayesian Network (BN), and compare them with each other. Furthermore, the research's central question More

  Background and Aim: The aim is first to express the differences and identify three models, namely, Gene Expression Programming (GEP), Neural-Fuzzy Network (ANFIS), and Bayesian Network (BN), and compare them with each other. Furthermore, the research's central question is whether the superior simulation meta-modal in this study can be a suitable alternative to conceptual models in the conditions of lack of data and information. Methods: The data used for this study are the daily rainfall and flow data of the Gamasiab Nahavand River in 10 years from 2002 to 2012. For the prediction or simulation stage, the data of the blue year 2012-2011 have been used. Results: In the training phase and according to the coefficient of explanation and the square root of the mean squares error and the AIC criterion, it is observed that in all three models, both in the training phase and in the test phase, we see a minimal difference in the amount of these parameters. Moreover, all three models' results are close to each other with almost a minimal difference, and almost the relative superiority of the GEP model can be seen. Discussion & Conclusion: The results indicate that the simulator meta-model of gene expression has an excellent ability to simulate and predict the river's daily flow, this simulation meta-model can be a suitable alternative to models in the absence of data and information. Be conceptual. Also, the speed of implementation of the gene expression programming model was faster than other models and was able to provide results in a short time. Manuscript profile
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  2 - Comparison of Artificial Intelligence Algorithms in Daily River Flow Modeling
  Massome Zeinali Sohila Farzi Mohammad Reza Golabi feridon radmanesh
  One of the most important issues in water resources engineering is the prediction of river flow rates as one of the main sources of human water supply, which is important in terms of water resources planning. Using new models in this field can help to manage and plan co More

  One of the most important issues in water resources engineering is the prediction of river flow rates as one of the main sources of human water supply, which is important in terms of water resources planning. Using new models in this field can help to manage and plan correctly. In this study, we evaluated 3 models called Neural-Fuzzy Network (ANFIS), Busin Network (BN) and Backup Machine Vector (SVM). The data used for this research is precipitation data and daily flow of Gamasiab Nahavand River during a 10 year period (1381-1391). The results indicated that the neural-fuzzy network model (ANFIS) and backup vector machine (SVM) had almost the same performance in daily river flow modeling and had better performance than the network model. In addition, the speed of implementation of SVM model compared to the rest The models were bigger and were able to deliver results in a short time. Manuscript profile