Application of Acoustic Tomographic Data in Short-Term Forecasting of Streamflow Using Combinatorial GMDH Algorithm (CGA)

Document Type : Research Article

Authors

1 MSC Student, Iran University of Science and Technology / Faculty of Civil Engineering

2 Professor, Iran University of Science and Technology / Faculty of Civil Engineering

3 Assistant Professor, Water Research Institute

4 Assistant Professor, Iran University of Science and Technology / Faculty of Civil Engineering

Abstract

Short-term forecasting of streamflow is one of the most important goals in water resources management and flood control. However, one of the problems that researchers always face in this type of prediction is the Lack of an accurate and high-resolution database. The Fluvial Acoustic Tomography (FAT) is an innovative technology that acquires streamflow data. Therefore, by using the data collected from this technology with a suitable forecast model, accurate short-term streamflow forecasting can be achieved. In this research, the effect of FAT data on short-term streamflow forecasting by the Combinatorial GMDH Algorithm (CGA) has been investigated and compared with one obtained from the Rating Curve method. The k-fold cross-validation criterion has been used to prevent over-fitting. The results showed that the FAT data increases the accuracy of short-term forecasting. As an example, the Nash-Sutcliffe coefficient (ENS) for the 1, 6, 12, 24, 48, and 72 hours forecast horizons were 0.98, 0.96, 0.94, 0.88, 0.73, and 0.54, respectively. While these values for the Rating Curve ones were 0.97, 0.84, 0.61, 0.27, 0.12, and 0.11, respectively.

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References

[1] M. Abbasi, A. Farokhnia, M. Bahreinimotlagh, R. Roozbahani, A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction, Journal of Hydrology,  (2020) 125717.
[2] M. Ehteram, F. Binti Othman, Z. Mundher Yaseen, H. Abdulmohsin Afan, M. Falah Allawi, A. Najah Ahmed, S. Shahid, V. P Singh, A. El-Shafie, Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm, Water, 10(6) (2018) 807.
[3] M.S. Khan, P. Coulibaly, Bayesian neural network for rainfall‐runoff modeling, Water Resources Research, 42(7) (2006).
[4] A. Mosavi, P. Ozturk, K.-w. Chau, Flood prediction using machine learning models: Literature review, Water, 10(11) (2018) 1536.
[5] R. Samsudin, P. Saad, A. Shabri, A hybrid least squares support vector machines and GMDH approach for river flow forecasting, Hydrology and Earth System Sciences Discussions, 7(3) (2010) 3691-3731.
[6] R. Walton, A. Binns, H. Bonakdari, I. Ebtehaj, B. Gharabaghi, Estimating 2-year flood flows using the generalized structure of the Group Method of Data Handling, Journal of Hydrology, 575 (2019) 671-689.
[7] P. Aghelpour, V. Varshavian, Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series, Stochastic Environmental Research and Risk Assessment, 34(1) (2020) 33-50.
[8] K. Kawanisi, M. Razaz, A. Kaneko, S. Watanabe, Long-term measurement of stream flow and salinity in a tidal river by the use of the fluvial acoustic tomography system, Journal of Hydrology, 380(1-2) (2010) 74-81.
[9] M. Bahreinimotlagh, K. Kawanisi, M.B. Al Sawaf, R. Roozbahani, M. Eftekhari, A.K. Khoshuie, Continuous streamflow monitoring in shared watersheds using advanced underwater acoustic tomography system: a case study on Zayanderud River, Environmental monitoring and assessment, 191(11) (2019) 1-9.
[10] M. Bahreinimotlagh, K. Kawanisi, X.-H. Zhu, Acoustic Investigations of Tidal Bores in Qiantang Rive, 59 (2015) I_139-144.
[11] K. Kawanisi, M. Bahreinimotlagh, M. Razaz, Energy flux measurement of tidal stream in a strait using two crossing ultrasonic transmission lines, 36th World Congr. Int. Assoc. Hydro-Environment Eng. Res.(IAHR-APD 2015), Hague, Netherlands,  (2015) 1-4.
[12] K. Kawanisi, A. Kaneko, S. Nigo, M. Soltaniasl, M.F. Maghrebi, New acoustic system for continuous measurement of river discharge and water temperature, Water Science and Engineering, 3(1) (2010) 47-55.
[13] K. Kawanisi, M.B. Al Sawaf, M.M. Danial, Automated real-time streamflow acquisition in a mountainous river using acoustic tomography, Journal of Hydrologic Engineering, 23(2) (2018) 04017059.
[14] M.B. Al Sawaf, K. Kawanisi, Novel high-frequency acoustic monitoring of streamflow-turbidity dynamics in a gravel-bed river during artificial dam flush, Catena, 172 (2019) 738-752.
[15] M. Bahreinimotlagh, R. Roozbahani, M. Eftakhari, M.H. KARDAN, S.A. Hasanli, Continuous Monitoring of Tidal Bores Using Acoustic Tomography Technique,  (2019). (in Persian)
[16] M. Bahreinimotlagh, R. Roozbahani, M. Eftekhari, A.K. Heydari, S. Abolhosseini, Investigation of Current Status in Haftbarm Lake Using Acoustic Tomography Technology, Journal of Water and Soil, 33 (2019). (in Persian)
[17] M. Bahreinimotlagh, R. Roozbahani, M.J. Zareian, H. Kardan Moghadam, K. Mohtasham, The continuous water temperature monitoring by using Acoustic Tomography Technology, Amirkabir Journal of Civil Engineering, 51(5) (2018) 18-18. (in Persian)
[18] M. Bahreinimotlagh, K. Kawanisi, A. Kavousi, R. Roozbahani, M. Abbasi, M.B. Al Sawaf, Influence of Suspended Sediment Concentration and Particle Sizes on the Sound Attenuation of the Fluvial Acoustic Tomography Technique, Journal of Water and Environment Technology, 18(5) (2020) 338-348.
[19] M. Bahreinimotlagh, I. Khaki, R. Roozbahani, Y. Zohrabi, H. Kardan Moghaddam, Range-and depth averaged temperature measurement of the coastal sea using Acoustic Tomography technique, Journal of Natural Environment, 73(4) (2021) 637-647. (in Persian)
[20] M. Bahreinimotlagh, R. Roozbahani, Y. Zohrabi, H. Kardanmoghadam, H. Dehban, K. Mohtasham, Feasibility study of real-time and automated monitoring of Iranian Rivers using 50-kHz fluvial acoustic tomography system, Journal of Acoustical Engineering Society of Iran, 8(1) (2020) 14-21. (in Persian)
[21] M. Bahreinimotlagh, R. Roozbahani, M. Eftekhari, H. Kardanmoghadam, M. Abbasi, K. Mohtasham, Feasibility study of Fluvial Acoustic Tomography System for flood monitoring and determination of the measurement accuracy, minimum and maximum measurement ranges, Iranian journal of Ecohydrology, 6(3) (2019) 585-592. (in Persian)
[22] M. Bahreinimotlagh, K. Kawanisi, M.M. Danial, M.B. Al Sawaf, J. Kagami, Application of shallow-water acoustic tomography to measure flow direction and river discharge, Flow Measurement and Instrumentation, 51 (2016) 30-39.
[23] T. Chai, R.R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE), Geoscientific Model Development Discussions, 7(1) (2014) 1525-1534.
[24] Z.M. Yaseen, I. Ebtehaj, H. Bonakdari, R.C. Deo, A.D. Mehr, W.H.M.W. Mohtar, L. Diop, A. El-Shafie, V.P. Singh, Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model, Journal of Hydrology, 554 (2017) 263-276.
[25] J.E. Nash, J.V. Sutcliffe, River flow forecasting through conceptual models part I—A discussion of principles, Journal of hydrology, 10(3) (1970) 282-290.
Amirkabir Journal of Civil Engineering
Volume 54, Issue 5
August 2022
Pages 1755-1772

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