Performance comparison of Artificial Intelligence models with IHACRES model in streamflow modeling of the Gamasiab River catchment

Document Type : Research/Original/Regular Article

Author

Graduated M.Sc. student/ Department of Civil Engineering, Faculty of Technical Engineering, Razi University, Kermanshah, Iran.

Abstract

Introduction
Today due to climate change and fluctuations in the intensity and duration of rainfall in most parts of the world, new approaches to streamflow modeling have an extraordinary role in managing water resources and reducing the risks of floods. Since in some catchments, it is not feasible to measure all the observed quantities required for modeling the streamflow process, it is necessary to choose a simple model that can accurately predict rainfall-runoff using minimal information. Artificial intelligence (AI) models have high efficiency, especially when the accurate estimation of processes is more important than understanding the mechanisms and relationships that create them. Therefore, the AI models and semi-conceptual IHACRES models are used for streamflow modeling and compared with each other. In this study, streamflow modeling for the Gamasiab River, located in western Iran, is presented.
Materials and Methods
For rainfall-runoff modeling, artificial intelligence (AI) models include artificial neural network (ANN) models of type multi-layer perceptron (MLP), radial basis function neural network (RBF), and long short-term memory model (LSTM) are used. In addition, to better evaluate the AI models, a specialized semi-conceptual rainfall-runoff model called IHACRES is used. The data used in this study include daily data of flow discharge, precipitation, and average temperature for 31 years (September 23, 1986 - September 22, 2017), which is a time series of delayed data and as an input signal used to models. To evaluate the performance of the models, some criteria including Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R) were used.
Results and Discussion
The results show better performance of ANN, RBF, and LSTM models than the IHACRES model, especially at the peak flow rate, in modeling daily streamflow for the study area. The IHACRES model has performed well in calm and medium flows but has not performed well at flow peaks. In addition, the LSTM model performed better than the other models in estimating the flow rate during the verification period, while the ANN and RBF models performed better than the other models in the calibration phase. Overall, the results indicate that the best data-based model (i.e., LSTM model) has more than twice as good performance in streamflow modeling as the semi-conceptual IHACRES model based on RMSE criteria. In general, the results showed that artificial intelligence models are valuable tools for modeling streamflow fluctuations.
Conclusion
The AI models including, ANN, RBF, and LSTM models, especially in estimating flow peaks, were significantly better than the IHACRES model. The IHACRES model has performed well in calm river streamflow and low and medium discharge but has not performed well at flow peaks. In general, is recommended the LSTM model for modeling the daily streamflow of the study area due to better performance. AI models can model the streamflow more accurately and provide more efficient management of water resources in different regions. In general, the results showed that various AI models are a suitable tool in streamflow modeling, and it suggested that they be more utilized in future research.

Keywords

References

Adamowski, J., & Chan, H.F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407, 28-40. doi:10.1016/j.jhydrol.2011.06.013.
Ahmadi, M., Moeini, A., Ahmadi, H., Motamedvaziri, B., & Zehtabiyan, G.R. (2019). Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran). Physics and Chemistry of the Earth, 111, 65-77. doi:10.1016/j.pce.2019.05.002.
Ahooghalandari, M., Khiadani, M., & Kothapalli, G., (2016). Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia. Australian Journal of Water Resources, 19, 116-126. doi:10.1080/13241583.2015.1116183.
Amiri, E., Roudbari Mousavi, M.M. (2016). Evaluation of IHACRES hydrological model for simulation of daily flow (case study Polrood and Shalmanrood rivers). Iranian Journal of Eco Hydrology, 3(4), 533-543 (in Persian).
Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., Santhi, C., Harmel, R.D., Van Griensven, A., Van Liew, M.W., Kannan, N., & Jha, M.K. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55, 1491-1508.
ASCE, (2000). Artificial Neural Networks in Hydrology II: Hydrologic Applications. Journal of Hydrologic Engineering, 5, 124-137. doi:10840699/00/0002-01240137.
Beck, M.B. (1991). Forecasting environmental change. Journal of Forecasting, 10, 3-19. doi:10.1002/for.3980100103.
Carcano, E.C., Bartolini, P., Muselli, M., & Piroddi, L. (2008). Jordan recurrent neural network versus IHACRES in modelling daily streamflows. Journal of Hydrology, 362, 291-307. doi:10.1016/j.jhydrol.2008.08.026.
Chen, L.H., Chen, C.T., & Pan, Y.G. (2010). Groundwater Level Prediction Using SOM-RBFN Multisite Model. Journal of Hydrologic Engineering, 15, 624-631. doi:10.1061/(asce)he.1943-5584.0000218.
Coulibaly, P., Anctil, F., & Bobee, B. (2000). Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology, 230, 244-257. doi:10.1016/S0022-1694(00)00214-6.
Croke, B.F.W., & Jakeman, A.J. (2007). Use of the IHACRES rainfall-runoff model in arid and semi-arid regions. Hydrological Modelling in Arid and Semi-Arid Areas, 41–48. doi:10.1017/CBO9780511535734.005.
Croke, B.F.W., Andrews, F., Jakeman, A.J., Cuddy, S.M., & Luddy, A. (2006). IHACRES Classic Plus: A redesign of the IHACRES rainfall-runoff model. Environmental Modelling & Software, 21, 426 - 427.
Ebrahimi, H., & Rajaee, T. (2017). Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change, 148, 181-191. doi:10.1016/j.gloplacha.2016.11.014.
Gong, Y., Zhang, Y., Lan, S., & Wang, H. (2016). A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida. Water Resources Management, 30, 375-391. doi:10.1007/s11269-015-1167-8.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Hsu, K.l., Gupta, H.V., Adamowski, J., Fung Chan, H., Prasher, S.O., Ozga-Zielinski, B., & Sliusarieva, A. (1995). Artificial neural network modeling of the rainfall-runoff process that arise and based Background and Scope. Water Resources Research, 48, 1-14.
Jakeman, A., Littlewood, I., & Whitehead, P. (1993). An assessment of the dynamic response characteristics of streamflow in the Balquhidder catchments. Journal of Hydrology, 145(3), 337-355.
Jha, M.K., & Sahoo, S. (2015). Efficacy of neural network and genetic algorithm techniques in simulating spatiotemporal fluctuations of groundwater. Hydrological Processes, 29, 671-691. doi:10.1002/hyp.10166.
Jimeno-Saez, P., Senent-Aparicio, J., Perez-Sanchez, J., & Pulido-Velazquez, D. (2018). A comparison of SWAT and ANN models for daily runoff simulation in different climatic zones of peninsular Spain. Water (Switzerland), 10. doi:10.3390/w10020192.
Kagoda, P.A., Ndiritu, J., Ntuli, C., & Mwaka, B. (2010). Application of radial basis function neural networks to short-term streamflow forecasting. Physics and Chemistry of the Earth, 35, 571-581. doi:10.1016/j.pce.2010.07.021.
Kang, K.W., Kim, J.H., Park, C.Y., & Ham, K. J. (1993). Evaluation of hydrological forecasting system based on neural network model. In: Proceedings of the 25th Congress of the International Association for Hydraulic Research, Delft, Netherlands, Pp. 257-264.
Kegl, B., Krzyak, A., & Niemann, H. (2000). Radial basis function networks and complexity regularization in function learning and classification. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, IEEE Comput. Soc, Barcelona, Spain, Pp. 81-86. doi:10.1109/ICPR.2000.906022.
Kim, J.W., & Pachepsky, Y.A. (2010). Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. Journal of Hydrology, 394, 305-314, doi:10.1016/j.jhydrol.2010.09.005.
Kim, H., & Lee, S. (2014). Assessment of a seasonal calibration technique using multiple objectives in rainfall–runoff analysis. Hydrological Processes, 28(4), 2159-2173.
Kim, K.B. (2015). Application of a baseflow filter for evaluating model structure suitability of the IHACRES CMD. Journal of Hydrology, 521, 543-555. doi:10.1016/j.jhydrol.2014.12.030.
Kim, K.B., Kwon, H-H., & Han, D. (2018). Exploration of warm-up period in conceptual hydrological modelling. Journal of Hydrology, 556, 194-210, doi:10.1016/j.jhydrol.2017.11.015.
Kim, T.W., & Valdes, J.B. (2003). Nonlinear Model for Drought Forecasting Based on a Conjunction of Wavelet Transforms and Neural Networks. Journal of Hydrologic Engineering, 8, 319-328, doi:10.1061/(asce)1084-0699(2003)8:6(319).
Kumar, D.N., Raju, K.S., & Sathish, T., (2004). River flow forecasting using artificial neural networks. Water Resources Management, 2, 143-161, doi:10.1016/j.asoc.2019.04.024.
Letcher, R., Schreider, S.Y., Jakeman, A., Neal, B., & Nathan, R. (2001). Methods for the analysis of trends in streamflow response due to changes in catchment condition. Environmetrics, 12(7), 613-630, doi:10.1002/env.486
Meng, X., Yin, M., Ning, L., Liu, D., & Xue, X. (2015). A threshold artificial neural network model for improving runoff prediction in a karst watershed. Environmental Earth Sciences, 74, 5039-5048. doi:10.1007/s12665-015-4562-9.
Moriasi, D. N., Arnold, J. G., Van Liew, M.W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885−900 doi:10.13031/2013.23153.
Motovilov, Y.G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98–99, 31, 257-277, doi:10.1016/S0168-1923(99)00102-1.
Mubialiwo, A., Abebe, A., & Onyutha, C. (2021). Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment. SN Applied. Sciences, 3, 515, doi:10.1007/s42452-021-04514-7.
Nayak, P.C., Satyaji Rao, Y.R., & Sudheer, K.P. (2006). Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management, 20, 77-90, doi:10.1007/s11269-006-4007-z.
Nourani, V., Baghanam, A.H., Rahimi, A.Y., & Nejad, F.H. (2014). Evaluation of Wavelet-Based De-noising Approach in Hydrological Models Linked to Artificial Neural Networks. Pp. 209-241, In: Islam, T., Srivastava, P.K., Gupta, M., Zhu, X., & Mukherjee, S. (Eds), Computational Intelligence Techniques in Earth and Environmental Sciences, Springer Netherlands, Dordrecht. volume 9789401786, doi:10.1007/978-94-017-8642-3_12.
Onyutha, C. (2019). Hydrological model supported by a stepwise calibration against sub-flows and validation of extreme flow events. Water, 11, 244, https://doi.org/10.3390/w11020244.
Principe, J.C., Euliano, N.R., & Curt Lefebvre, W. (1999). Neural and Adaptive Systems: Fundamentals through Simulations. Wiley, 672 pages.
Panda, R.K., Pramanik, N., & Bala, B., (2010). Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Computers and Geosciences, 36, 735-745, doi:10.1016/j.cageo.2009.07.012.
Pramanik, N., & Panda, R.K. (2009). Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrological Sciences Journal, 54, 247-260, doi:10.1623/hysj.54.2.247.
Rezaeianzadeh, M., Stein, A., Tabari, H., Abghari, H., Jalalkamali, N., Hosseinipour, E.Z., & Singh, V.P. (2013). Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. International Journal of Environmental Science and Technology, 10, 1181-1192, doi:10.1007/s13762-013-0209-0.
Sen, S.Y., & Ozkurt, N. (2020). Convolutional Neural Network Hyperparameter Tuning with Adam Optimizer for ECG Classification. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, 978, Pp. 1-6. doi:10.1109/ASYU50717.2020.9259896.
Solomatine, D.P., & Ostfeld, A. (2008). Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics, 10, 3-22, doi:10.2166/hydro.2008.015.
Taesombat, W., & Sriwongsitanon, N. (2010). Flood investigation in the Upper Ping River Basin using mathematical models. Kasetsart Journal-Natural Science, 44, 152-166.
Tokar, B.A.S., & Johnson, P.A. (1999). Rainfall-Runoff modeling using artificial neural networks. Journal of Hydrologic Engineering, 4, 232-239.
Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J. M., Viney, N. R., & Teng, J. (2010). Climate non-stationarity–validity of calibrated rainfall–runoff models for use in climate change studies. Journal of Hydrology, 394(3), 447-457.
Wagena, M.B., Goering, D., Collick, A.S., Bock, E., Fuka, D.R., Buda, A., & Easton, Z.M. (2020). Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models. Environmental Modelling and Software, 126. doi:10.1016/j.envsoft.2020.104669.
Wu, C., Zhang, X., Wang, W., Lu, C., Zhang, Y., & Qin, W., et al. (2021). Groundwater level modeling framework by combining the wavelet transform with a long short-term memory data-driven model. Science of The Total Environment, 783, 146948, doi:10.1016/j.scitotenv.2021.146948.
Ye, W., Bates, B.C., Viney, N.R., Sivapalan, M., & Jakeman, A.J. (1997). Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments. Water Resources  Research, 33, 153–166.
Young, C.C., Liu, W.C., & Wu, M.C. (2017). A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Applied Soft Computing Journal, 53, 205-216, doi:10.1016/j.asoc.2016.12.052.
Zareian, M.J., Eslamian, S., Gohari, A., & Adamowski, J.F. (2017). The effect of climate change on watershed water balance. Pp. 215–238, In: Mathematical Advances towards Sustainable Environmental Systems, Springer.
Zarezade Mehrizi, S., Khoorani, A., Bazrafshan, J., & Bazrafshan, O. (2018). Assessing the efficiency of SWAT model for runoff simulation in Gamasiyab basin. Rangeland and Watershed Management. Iranian Journal of Natural Resources, 70(4), 881-893 (in Persian).
Zeinoddini, S., Anvari, S., & Zahmatkesh, Z. (2018). Application of Simulation-Optimization Approaches to Assess the Effect of Climate and Management Scenarios on a Water Resource System. Iran-Water Resources Research, 14(5), 295-310 (in Persian).
Zhang, G., Patuwo, B., & Hu, M.Y. (2001). A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers & Operations Research, 28, 381-396, doi:10.1016/S0305-0548(99)00123-9.
Zhu, S., Luo, X., Yuan, X., & Xu, Z. (2020). An improved long short-term memory network for streamflow forecasting in the upper Yangtze River. Stochastic Environmental Research and Risk Assessment, 34, 1313-1329, doi:10.1007/s00477-020-01766-4.
Water and Soil Management and Modelling
Volume 2, Issue 3
2022
Pages 1-16

Files

History

  • Receive Date: 09 December 2021
  • Revise Date: 15 January 2022
  • Accept Date: 20 January 2022

Share

How to cite

Statistics