Evaluation of Time Series Analysis Based on Wavelet Function on River Flow Simulation

Subject Areas : Water resources management

Vahed Eslamitabar ¹ (Phd student, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Ahmad Sharafati ² (Associate professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Farshad Ahmadi ³ (Assistant Professor, Water Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran.)
Vahid Rezaverdinejad ⁴ (Professor, Department of Water Engineering, Urmia University, Urmia.Iran.)

Received: 2023-09-26 Accepted : 2023-12-22 Published : 2023-12-22

Keywords: Random forest, Wavelet Transform, Daubechies, Haar wavelet,

Abstract :

Introduction: Using the values analyzed by the wavelet function can increase the accuracy of the simulations. Considering the climatic changes and the increase of extreme values in recent years, in this study, we made an effort that the effect of signal processing under the name of wavelet transformation in improving the performance of random forest model in simulating monthly river flow in Siminehrood and Mahabadchai sub-basins in the south of Lake Urmia has been discussed and investigated in the period of 1971-2019. Materials and Methods: In this study, the accuracy of the random forest model has been investigated in two steps of training and testing. At first, the random forest model was evaluated in two phases of training and testing in rainfall-runoff simulation in the south of Lake Urmia basin. Nash-Sutcliffe statistics and root mean square error were used to evaluate the performance and error rate of the studied models, respectively. In the next step, after investigating the performance of the random forest model, the time series of rainfall and river flow in the studied basins were analyzed using the wavelet function. In this regard, two analysis levels (level 1 and 2) and two Haar and Daubechies wavelet functions were used. Finally, using the random forest model, rainfall-runoff simulation based on the wavelet theory was done under the name of W-RF model. Results and Dissection: At First, the random forest model was investigated in two phases of training and testing, and the simulation results of the river flow values showed that the simulated values were within the 95% confidence interval, and the error rate of the river flow simulation using the RMSE statistic is 3.22 and 8.91 cubic meters per second in the test phase for Mahabadchai and Siminehrood sub-basins, respectively. In order to investigate the effect of time series analysis on the performance of the RF model, wavelet theory and Haar and Daubechies 4 wavelet functions were used in decomposition levels 1 and 2. By estimating the accuracy and performance of the hybrid W-RF model in 4 input patterns, the best pattern was selected based on the RMSE and NSE model evaluation criteria. The research results showed that for the Haar wavelet function in level 1 decomposition has better performance and error rate than level 2 type in both sub-basins. In this study, the Daubechies wavelet at level 1 in the test phase has provided the best performance and the lowest error rate in the simulation of the river flow values in the studied sub-basins and has been able to reduce the error rate in the two sub-basins of Mahabadchai and Siminehrood respectively by about 89 and 80 percent compared to the random forest model. Conclusion: Finally, by comparing the RF and W-RF models, the simulation results of river flow in the two studied sub-basins showed that the integrated W-RF model was able to reduce the error rate in the two sub-basins of Mahabadchai and Siminehrood to reduce by 89 and 80% respectively. Considering the increase in simulation complexity with the involvement of wavelet theory, the error recovery rate and model performance are acceptable. The integrated W-RF model in this study provides reliable results for the simulation of river flow data in order to support decision-making and risk analysis in the exploitation of downstream reservoirs and the management of water resources in sub-basins. The obtained results can be used in the design of water resources systems.

References:

Adamowski, J., & Sun, K. (2010). Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390(1-2), 85-91.

Ahmadi, F. (2021). Evaluation of the Efficiency of Data Preprocessing Methods on Improving the Performance of Gene Expression Programming Model (Case Study: Ab Zal River). Journal of Water and Soil, 35(2), 153-165. [In Persian].

Asadi, S., Shahrabi, J., Abbaszadeh, P., & Tabanmehr, S. (2013). A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing, 121, 470-480.

Bageri, F., Khalili, K., & Nazeri Tahrudi, M. (2023). Evaluation of Entropy Theory Based on Random Forest in Quality Monitoring of Ground Water Network. Water and Irrigation Management, 13(1), 123-139

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

Campolo, M., Soldati, A., & Andreussi, P. (2003). Artificial neural network approach to flood forecasting in the River Arno. Hydrological Sciences Journal, 48(3), 381-398.

Cannas, B., Fanni, A., See, L., & Sias, G. (2006). Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Physics and Chemistry of the Earth, Parts A/B/C, 31(18), 1164-1171.

Danandeh Mehr, A., Kahya, E., & Olyaie, E. (2013). Streamflow prediction using linear genetic programming in comparison with a neurowavelet technique. J Hydrol 505:240–249.

Darbandi, S., & Pourhosseini, F. A. (2018). River flow simulation using a multilayer perceptron-firefly algorithm model. Applied Water Science, 8(3), 1-9.

Demyanov, V., Soltani, S., Kanevski, M., Canu, S., Maignan, M., Savelieva, E., ... & Pisarenko, V. (2001). Wavelet analysis residual kriging vs. neural network residual kriging. Stochastic Environmental Research and Risk Assessment, 15(1), 18-32.

Feng, Z. K., & Niu, W. J. (2021). Hybrid artificial neural network and cooperation search algorithm for nonlinear river flow time series forecasting in humid and semi-humid regions. Knowledge-Based Systems, 211, 106580.

Geshnigani, F. S., Golabi, M. R., Mirabbasi, R., & Tahroudi, M. N. (2023). Daily solar radiation estimation in Belleville station, Illinois, using ensemble artificial intelligence approaches. Engineering Applications of Artificial Intelligence, 120, 105839.

Hassanjabbar, A., Nezaratian, H., & Wu, P. (2022). Climate change impacts on the flow regime and water quality indicators using an artificial neural network (ANN): a case study in Saskatchewan, Canada. Journal of Water and Climate Change.

Hu, T. S., Lam, K. C., & Ng, S. T. (2005). A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial. Hydrological sciences journal, 50(2).

Jain, S. K., Das, A., & Srivastava, D. K. (1999). Application of ANN for reservoir inflow prediction and operation. Journal of water resources planning and management, 125(5), 263-271.

Jayawardena, A. W., Xu, P., & Tsang, F. L. L. (2004, July). Rainfall predication by wavelet decomposition. In Asia Pacific Association of Hydrology and Water Resources Conference, Singapore.

Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., & Pulido-Velazquez, D. (2018). A comparison of SWAT and ANN models for daily runoff simulation in different climatic zones of peninsular Spain. Water, 10(2), 192.

Kalhori, M., Ashofteh, P. S., Moghadam, S. H., & Singh, V. P. (2022). Investigating the effect of uncertainty of AOGCM-TAR and AOGCM-AR5 climate change models on river runoff. Arabian Journal of Geosciences, 15(13), 1-16.

Kisi, O., & Kerem Cigizoglu, H. (2007). Comparison of different ANN techniques in river flow prediction. Civil Engineering and Environmental Systems, 24(3), 211-231.

Mallat, S. G. (1989). Multiresolution approximations and wavelet orthonormal bases of 𝐿² (𝑅). Transactions of the American mathematical society, 315(1), 69-87.

Meresa, H. (2019). Modelling of river flow in ungauged catchment using remote sensing data: application of the empirical (SCS-CN), artificial neural network (ANN) and hydrological model (HEC-HMS). Modeling Earth Systems and Environment, 5(1), 257-273.

Mirzaee, N., & Sarraf, A. (2022). Application of data fusion models in river flow simulation using signals of large-scale climate, case study: Jiroft Dam Basin. Watershed Engineering and Management, 13(4), 672-689.

Mohanta, N. R., Patel, N., Beck, K., Samantaray, S., & Sahoo, A. (2021). Efficiency of river flow prediction in river using wavelet-CANFIS: a case study. In Intelligent Data Engineering and Analytics: Frontiers in Intelligent Computing: Theory and Applications (FICTA 2020), Volume 2 (pp. 435-443). Springer Singapore.

Nakken, M. (1999). Wavelet analysis of rainfall–runoff variability isolating climatic from anthropogenic patterns. Environmental Modelling & Software, 14(4), 283-295.

Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282-290.

Nazeri Tahroudi, M., Ahmadi, F., & Mirabbasi, R. (2023). Performance comparison of IHACRES, random forest and copula-based models in rainfall-runoff simulation. Applied Water Science, 13(6), 134.

Nekoeeyan, M. A., Radmanesh, F., & Ahmadi, F. (2022). Prediction of Monthly Streamflow Using Shannon Entropy and Wavelet Theory Approaches (Case study: Maroon River). Water and Irrigation Management, 12(1), 15-31. doi: 10.22059/jwim.2022.335702.949. [In Persian].

Nilsson, P., Uvo, C. B., & Berndtsson, R. (2006). Monthly runoff simulation: Comparing and combining conceptual and neural network models. Journal of Hydrology, 321(1-4), 344-363.

Noori, N., Kalin, L., & Isik, S. (2020). Water quality prediction using SWAT-ANN coupled approach. Journal of Hydrology, 590, 125220.

Nourani, V., Baghanam, A. H., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358-377.

Pronoos Sedighi, M., Ramezani, Y., Nazeri Tahroudi, M., & Taghian, M. (2023). Joint frequency analysis of river flow rate and suspended sediment load using conditional density of copula functions. Acta Geophysica, 71(1), 489-501.

Salerno, F., & Tartari, G. (2009). A coupled approach of surface hydrological modelling and Wavelet Analysis for understanding the baseflow components of river discharge in karst environments. Journal of hydrology, 376(1-2), 295-306.

Seifi, A., & Riahi-Madvar, H. (2019). Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models. Environmental Science and Pollution Research, 26(1), 867-885.

Sivakumar, B., Jayawardena, A. W., & Fernando, T. M. K. G. (2002). River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of hydrology, 265(1-4), 225-245.

Solgi, A., Pourhaghi, A., Bahmani, R., & Zarei, H. (2017a). Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Journal of Earth System Science, 126(5), 1-17.

Solgi, A., Zarei, H., Nourani, V., & Bahmani, R. (2017b). A new approach to flow simulation using hybrid models. Applied Water Science, 7(7), 3691-3706.

Wei, S., Yang, H., Song, J., Abbaspour, K., & Xu, Z. (2013). A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58(2), 374-389.

Zakhrouf, M., Bouchelkia, H., Stamboul, M., Kim, S., & Heddam, S. (2018). Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria). Physical Geography, 39(6), 506-522.

Zealand, C. M., Burn, D. H., & Simonovic, S. P. (1999). Short term streamflow forecasting using artificial neural networks. Journal of hydrology, 214(1-4), 32-48.

Zhou, J., Wang, D., Band, S. S., Jun, C., Bateni, S. M., Moslehpour, M., ... & Ameri, R. (2023). Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm. Water Resources Management, 1-20.