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Comparison of different methods for reconstruction of instantaneous peak flow data


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Abstract

In arid and semi-arid regions, documentary data of past floods remain justly rare and highly fragmentary in most cases. Existence of many effective parameters on maximum flood discharge and the complex relationships between them is an important challenge in the reconstruction of these data and hence, it limited the application of traditional methods. In this paper, an alternative approach (i.e. artificial intelligence methods) has been evaluated to determine the interactive relations of them. To this end, flow data was collected from 29 gauging stations in the central part of Iran for the period 1965 to 2007. Following quality and homogeneity controls of the data, reconstruction of instantaneous peak flow time series were made using maximum daily data by four different methods; regression method (REG), artificial neural network (ANN), genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS). Results showed that in all studied stations, ANFIS reconstructs instantaneous peak flow values with the highest accuracy among the four tested methods.


Keywords


Pages

Total Pages: 9
Pages: 41-49

DOI
10.1080/10798587.2015.1120991


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Published

Volume: 23
Issue: 1
Year: 2016

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References

Ahmad, Sajjad, and Slobodan P. Simonovic. "An Artificial Neural Network Model for Generating Hydrograph from Hydro-Meteorological Parameters." Journal of Hydrology 315.1-4 (2005): 236-251. Crossref. Web. https://doi.org/10.1016/j.jhydrol.2005.03.032

Amini, M. et al. "Neural Network Models to Predict Cation Exchange Capacity in Arid Regions of Iran." European Journal of Soil Science 56.4 (2005): 551-559. Crossref. Web. https://doi.org/10.1111/j.1365-2389.2005.0698.x

Amini, M. et al. "Neural Network Models to Predict Cation Exchange Capacity in Arid Regions of Iran." European Journal of Soil Science 56.4 (2005): 551-559. Crossref. Web. https://doi.org/10.1111/j.1365-2389.2005.0698.x

Chau, K.W. "An Ontology-Based Knowledge Management System for Flow and Water Quality Modeling." Advances in Engineering Software 38.3 (2007): 172-181. Crossref. Web. https://doi.org/10.1016/j.advengsoft.2006.07.003

Chau, K. W., C. L. Wu, and Y. S. Li. "Comparison of Several Flood Forecasting Models in Yangtze River." Journal of Hydrologic Engineering 10.6 (2005): 485-491. Crossref. Web. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:6(485)

Chen, W., and K.W. Chau. "Intelligent Manipulation and Calibration of Parameters for Hydrological Models." International Journal of Environment and Pollution 28.3/4 (2006): 432. Crossref. Web. https://doi.org/10.1504/IJEP.2006.011221

Cheng, Chuntian et al. "Long-Term Prediction of Discharges in Manwan Reservoir Using Artificial Neural Network Models." Lecture Notes in Computer Science (2005): 1040-1045. Crossref. Web. https://doi.org/10.1007/11427469_165

Cheng, Chun-Tian et al. "Long-Term Prediction of Discharges in Manwan Hydropower Using Adaptive-Network-Based Fuzzy Inference Systems Models." Advances in Natural Computation (2005): 1152-1161. Crossref. Web. https://doi.org/10.1007/11539902_145

Danandehmehr A. Water and Soil Journal

Dastorani, Mohammad T. et al. "Application of ANN and ANFIS Models for Reconstructing Missing Flow Data." Environmental Monitoring and Assessment 166.1-4 (2009): 421-434. Crossref. Web. https://doi.org/10.1007/s10661-009-1012-8

Elshorbagy, A. et al. "Experimental Investigation of the Predictive Capabilities of Data Driven Modeling Techniques in Hydrology - Part 2: Application." Hydrology and Earth System Sciences 14.10 (2010): 1943-1961. Crossref. Web. https://doi.org/10.5194/hess-14-1943-2010

Hassanpour Kashani M. Journal of Agriculture & Environmental Science

Levine, E.R., D.S. Kimes, and V.G. Sigillito. "Classifying Soil Structure Using Neural Networks." Ecological Modelling 92.1 (1996): 101-108. Crossref. Web. https://doi.org/10.1016/0304-3800(95)00199-9

Menhaj, M. B. (2007). Computational Intelligence Fundamentals of Neural Networks, Vol (2) . Amirkabir University of Technology Press 715p. (in Persian).

Minsky M. L. Perceptrons

Morshed, Jahangir, and Jagath J. Kaluarachchi. "Parameter Estimation Using Artificial Neural Network and Genetic Algorithm for Free-Product Migration and Recovery." Water Resources Research 34.5 (1998): 1101-1113. Crossref. Web. https://doi.org/10.1029/98WR00006

Muttil, Nitin, and Kwok Wing Chau. "Neural Network and Genetic Programming for Modelling Coastal Algal Blooms." International Journal of Environment and Pollution 28.3/4 (2006): 223. Crossref. Web. https://doi.org/10.1504/IJEP.2006.011208

Nayak, Purna C., Y. R. Satyaji Rao, and K. P. Sudheer. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach." Water Resources Management 20.1 (2006): 77-90. Crossref. Web. https://doi.org/10.1007/s11269-006-4007-z

Nayak, P.C et al. "A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series." Journal of Hydrology 291.1-2 (2004): 52-66. Crossref. Web. https://doi.org/10.1016/j.jhydrol.2003.12.010

Nayak, P. C. et al. "Short-Term Flood Forecasting with a Neurofuzzy Model." Water Resources Research 41.4 (2005): n. pag. Crossref. Web. https://doi.org/10.1029/2004WR003562

Nourani, Vahid, Reza Goli Ejlali, and Mohammad Taghi Alami. "Spatiotemporal Groundwater Level Forecasting in Coastal Aquifers by Hybrid Artificial Neural Network-Geostatistics Model: A Case Study." Environmental Engineering Science 28.3 (2011): 217-228. Crossref. Web. https://doi.org/10.1089/ees.2010.0174

Nourani, Vahid, Asghar Asghari Mogaddam, and Ata Ollah Nadiri. "An ANN-Based Model for Spatiotemporal Groundwater Level Forecasting." Hydrological Processes 22.26 (2008): 5054-5066. Crossref. Web. https://doi.org/10.1002/hyp.7129

Salajegheh A. Range and Watershed Management Journal

Sivapragasam, C., P. Vincent, and G. Vasudevan. "Genetic Programming Model for Forecast of Short and Noisy Data." Hydrological Processes 21.2 (2007): 266-272. Crossref. Web. https://doi.org/10.1002/hyp.6226

Talei, Amin, Lloyd Hock Chye Chua, and Chai Quek. "A Novel Application of a Neuro-Fuzzy Computational Technique in Event-Based Rainfall-runoff Modeling." Expert Systems with Applications 37.12 (2010): 7456-7468. Crossref. Web. https://doi.org/10.1016/j.eswa.2010.04.015

Taormina, Riccardo, Kwok-wing Chau, and Rajandrea Sethi. "Artificial Neural Network Simulation of Hourly Groundwater Levels in a Coastal Aquifer System of the Venice Lagoon." Engineering Applications of Artificial Intelligence 25.8 (2012): 1670-1676. Crossref. Web. https://doi.org/10.1016/j.engappai.2012.02.009

Wang, Wen-chuan et al. "Assessment of River Water Quality Based on Theory of Variable Fuzzy Sets and Fuzzy Binary Comparison Method." Water Resources Management 28.12 (2014): 4183-4200. Crossref. Web. https://doi.org/10.1007/s11269-014-0738-4

Wu, C. L., K. W. Chau, and Y. S. Li. "Predicting Monthly Streamflow Using Data-Driven Models Coupled with Data-Preprocessing Techniques." Water Resources Research 45.8 (2009): n. pag. Crossref. Web. https://doi.org/10.1029/2007WR006737

Yang, Z.P. et al. "Application and Comparison of Two Prediction Models for Groundwater Levels: A Case Study in Western Jilin Province, China." Journal of Arid Environments 73.4-5 (2009): 487-492. Crossref. Web. https://doi.org/10.1016/j.jaridenv.2008.11.008

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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