Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2014 by IJETT Journal | ||
| Volume-12 Number-10 |
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| Year of Publication : 2014 | ||
| Authors : Dushyant Patel , Dr. Falguni Parekh |
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 10.14445/22315381/IJETT-V12P295 |
Citation
Dushyant Patel , Dr. Falguni Parekh. "Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS) ", International Journal of Engineering Trends and Technology (IJETT), V12(10),510-514 June 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
The aim of the present study is to explore applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood for the case study, Dharoi Dam on the Sabarmati river near village Dharoi in Kheralu Taluka of Mehsana District in Gujarat State, India. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R2) and Discrepancy Ratio (D) are used to evaluate performance of the ANFIS models in forecasting flood. This objective is accomplished by evaluating the model by comparing ANFIS model to Statistical method like Log Pearson type-III method to forecasting flood. This comparison shows that ANFIS model can accurately and reliably be used to forecast flood in this study.
References
[1] A.K. Karl and A.K Lohani,. “Development of flood forecasting system using Statistical and ANN Techniques in the downstream catchment of Mahanadi Basin, India”. Journal of Water Resource and Protection, 2(10),880-887, 2010.
[2] Dinesh C. S. Bisht and A. Jangid, “Discharge Modelling using Adaptive Neuro - Fuzzy Inference System” International Journal of Advanced Science and Technology Vol. 31, June, 2011
[3] H. Galavi, and T. S. Lee,“Neuro-fuzzy modelling and forecasting in water resources”, Scientific Research and Essays, Vol. 7(24), pp. 2112-2121, 2012.
[4] Jang, J.-S.R.. ANFIS: Adaptive network based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685, 1993.
[5] Jayawardena, A. W. & Lai Feizhou “Analysis and Prediction of Chaos in Rainfall and Stream Flow Time Series”. J. Hydrol.153. 23-52,1994.
[6] K.W. Chau, C.L. Wu and Y.S. Li, “Comparison of several flood forecasting models in Yangtze River”, Journal of Hydrologic Engineering, ASCE, Vol. 10, No. 6, 2005, pp. 485-491, 2005.
[7] N.Ullah and P.Choudhury“Flood Forecasting in River System Using ANFIS”, American Institute of Physics, pp 694-699, 2010
[8] Nazrin Ullah ,“Flood Flow Modeling in a River System Using Adaptive Neuro-Fuzzy Inference System”, Environmental Management and Sustainable Development, Vol. 2,No. 2, 2013.
[9] R. D. Singh “Real time Flood Forecasting –India Experiences” National Institute of Hydrology, Roorkee, 1987.
References
Adaptive Neuro-Fuzzy Inference System, Flood forecasting, Statistical method.