Hybrid wavelet transform - MLR and ANN models for river flow prediction: Case study of Brahmaputra river (Pancharatna station)

• Autorzy: Khandekar Sachin Dadu , Aswar Dinesh Shrikrishna , Sabale Pandurang Digamber , Khandekar Varsha Sachin , Bajad Mohankumar Namdeorao , Khaple Shivakumar

Identyfikatory

DOI

10.22630/srees.5258

• Języki publikacji: EN

Abstrakty

EN

In this research, discrete wavelet transform (DWT) is combined with MLR and ANN to develop WMLR and WANN hybrid models, respectively, for the Brahmaputra river (Pancharatna station) flow forecasting. Daily flow data for the period of 10 year were decomposed (up to fifth level) into detailed and approximation coefficients (using Daubechies wavelets db1, db2, db3, db8 and db10) which were fed as input to MLR and ANN to get the predicted discharge values two days, four days, seven days and 14 days ahead. For all lead times, the WMLR-db10 model was found to be superior as compared to WANN-db1, WANN-db2, WANN-db3, WANN-db8, WMLR-db1, WMLR-db2, WMLR-db3, WMLR-db8 and single MLR and ANN models. During testing period, the values of determination coefficient (R²) and RMSE for WMLR-db10 model for two-, four-, seven- and 14-day lead time were found to be, respectively, 0.996 (751.87 m³·s^–1), 0.991 (1,174.80 m³·s^–1), 0.984 (1,585.02 m³·s^–1), and 0.968 (2,196.46 m³·s^–1). Also, it was observed that for lower order wavelets (db1, db2, db3) WANN’s performance was better, and for higher order wavelets (db8, db10) WMLR’s performance was better. Correspondingly, it was observed that all hybrid models’ efficiency increased with increase in the decomposition level.

Słowa kluczowe

EN

wavelet transform; artificial neural network; multiple linear regression; streamflow; Daubechies wavelet; time series

• Wydawca: Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
• Czasopismo: Przegląd Naukowy Inżynieria i Kształtowanie Środowiska
• Rocznik: 2024
• Tom: Vol. 33, No. 1
• Strony: 69--94
• Opis fizyczny: Bibliogr. 51 poz., mapy, rys., tab., wykr.

Twórcy

autor

Khandekar Sachin Dadu

• Sachin Dadu Khandekar Savitribai Phule Pune University, Sinhgad College of Engineering, Maharashtra, India

• https://orcid.org/0000-0001-9091-6415

autor

Aswar Dinesh Shrikrishna

• Savitribai Phule Pune University, Sinhgad College of Engineering, Maharashtra, India

autor

Sabale Pandurang Digamber

• Deccan College Deemed University, Senior Professor and Head, Maharashtra, India

autor

Khandekar Varsha Sachin

• Analytics-Analyst, Cummins Global Services & Analytics India Office Campus, Pune (Maharashtra), India

autor

Bajad Mohankumar Namdeorao

• Savitribai Phule Pune University, Sinhgad College of Engineering, Maharashtra, India

autor

Khaple Shivakumar

• JSPM Narhe Technical Campus, Narhe, SPPU, Maharashtra, India

Bibliografia

• 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, 85-91. https://doi.org/10.1016/j.jhydrol.2010.06.033
• Araghi, A., Adamowski, J. & Martinez, C. J. (2020). Comparison of wavelet-based hybrid models for the estimation of daily reference evapotranspiration in different climates. Journal of Water and Climate Change, 11, 39-53. https://doi.org/10.2166/wcc.2018.113
• ASCE Task Committee on Application of Artificial Neural Network in Hydrology (2000). Artificial neural networks in hydrology-II: hydrologic applications. Journal of Hydrologic Engineering, 5 (2), 124-137.
• Bajirao, T. S., Kumar, P., Kumar, M., Elbeltagi, A. & Kuriqi, A. (2021). Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins. Theoretical and Applied Climatology, 145 (3-4), 1207-1231. https://doi.org/10.1007/s00704-021-03681-2
• Besaw, L. E., Rizzo, D. M., Bierman, P. R. & Hackett, W. R. (2010). Advances in ungauged stream flow prediction using artificial neural networks. Journal of Hydrology, 386 (1-4), 27-37. https://doi.org/10.1016/j.jhydrol.2010.02.037
• Budu, K. (2013). Comparison of wavelet-based ANN and regression models for reservoir inflow forecasting. Journal of Hydrologic Engineering, 19 (7), 1385-1400. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000892
• Dawson, C. W. & Wilby, R. (1998). An artificial neural network approach to rainfall runoff modeling. Hydrological Sciences Journal, 43 (1), 47-66. https://doi.org/10.1080/02626669809492102
• Dawson, C. W. & Wilby, R. L. (2001). Hydrological modelling using artificial neural network. Progress in Physical Geography: Earth and Environment, 25 (1), 80-108. https://doi.org/10.1177/030913330102500104
• Deka, P. C. & Prahlada, R. (2012). Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time. Ocean Engineering, 43, 32-42. https://doi.org/10.1016/j.oceaneng.2012.01.017
• Djerbouai, S. & Souag-Gamane, D. (2016). Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resources Management, 30, 2445-2464. https://doi.org/10.1007/s11269-016-1298-6
• Goswami, D. C. (1985). Brahmaputra River, Assam, India: physiography, basin denudation, and channel aggradation. Water Resources Research, 21 (7), 959-978. https://doi.org/10.1029/WR021i007p00959
• Jain, S. K. & Chalisgaonkar, D. (2000). Setting up stage-discharge relations using ANN. Journal of Hydrologic Engineering, 5 (4), 428-433. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:4(428)
• Katipoğlu, O. M. (2023a). Monthly streamflow prediction in Amasya, Türkiye, using an integrated approach of a feedforward backpropagation neural network and discrete wavelet transform. Modeling Earth Systems and Environment, 9 (2), 2463-2475. https://doi.org/10.1007/s40808-022-01629-7
• Katipoğlu, O. M. (2023b). Evaluation of the success of the hybrid wavelet-based ANFIS approach in the estimation of monthly stream flows of the Bitlis River, Turkey. Water Supply, 23 (2), 836-850. https://doi.org/10.2166/ws.2023.024
• Katipoğlu, O. M. (2023c). Evaporation prediction with wavelet-based hyperparameter optimized K-nearest neighbors and extreme gradient boosting algorithms in a semi-arid environment. Environmental Processes, 10 (4), 50. https://doi.org/10.1007/s40710-023-00669-0
• Katipoğlu, O. M. (2023d). Combining discrete wavelet decomposition with soft computing techniques to predict monthly evapotranspiration in semi-arid Hakkâri province, Türkiye. Environmental Science and Pollution Research, 30 (15), 44043-44066. https://doi.org/10.1007/s11356-023-25369-y
• Katipoğlu, O. M. (2023e). Prediction of streamflow drought index for short-term hydrological drought in the semi-arid Yesilirmak Basin using Wavelet transform and artificial intelligence techniques. Sustainability, 15 (2), 1109. https://doi.org/10.3390/su15021109
• Khandekar, S. D. (2014). Hybrid wavelet transform-neural network approach for short term and long term time series flow forecasting (PhD thesis). National Institute of Technology Karnataka, Mangalore.
• Khandekar, S. D. & Deka, P. C. (2016). Applications of wavelet transform technique in hydrology – a brief review. In A. Sarma, V. Singh, S. Kartha & R. Bhattacharjya (Eds), Urban Hydrology, Watershed Management and Socio-Economic Aspects. Water Science and Technology Library. Vol. 73. Cham: Springer. https://doi.org/10.1007/978-3-319-40195-9_19
• Khazaee Poul, A., Shourian, M. & Ebrahimi, H. (2019). A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resources Management, 33, 2907-2923. https://doi.org/10.1007/s11269-019-02273-0
• Kişi, Ö. (2009a). Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrological Processes, 23 (25), 3583-3597. https://doi.org/10.1002/hyp.7461
• Kişi, Ö. (2009b). Neural networks and wavelet conjunction model for intermittent stream flow forecasting. Journal of Hydrologic Engineering, 14 (8), 773-782. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000053
• Kişi, Ö. (2010). Wavelet regression model for short-term stream flow forecasting. Journal of Hydrology, 389 (3-4), 344-353. https://doi.org/10.1016/j.jhydrol.2010.06.013
• Kişi, Ö. (2011). Wavelet regression model as an alternative to neural networks for river stage forecasting. Water Resource Management, 25, 579-600. https://doi.org/10.1007/s11269-010-9715-8
• Kumar, M., Kumar, P., Kumar, A., Elbeltagi, A. & Kuriqi, A. (2022). Modeling stage–discharge–sediment using support vector machine and artificial neural network coupled with wavelet transform. Applied Water Science, 12 (5), 87. https://doi.org/10.1007/s13201-022-01621-7
• Labat, D., Ababou, R. & Mangin, A. (2000). Rainfall–runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses. Journal of Hydrology, 238 (3-4), 149-178. https://doi.org/10.1016/S0022-1694(00)00322-X
• Linh, N. T. T., Ruigar, H., Golian, S., Bawoke, G. T., Gupta, V., Rahman, K. U., Sankaran, A. & Pham, Q. B. (2021). Flood prediction based on climatic signals using wavelet neural network. Acta Geophysica, 69 (4), 1413-1426. https://doi.org/10.1007/s11600-021-00620-7
• Mallat, S. G. (1989). Multiresolution approximations and wavelet orthonormal bases of L2 (R). Transactions of the American Mathematical Society, 315 (1), 69-87.
• Mallat, S. G. (1998). A wavelet tour of signal processing. San Diego: Academic Press.
• Misiti, M., Misiti, Y., Oppenheim, G. & Poggi, J. (2010). Wavelet toolbox: For use with MATLAB. The MathWorks, Natick.
• Mohseni, U. & Muskula, S. B. (2023). Rainfall-runoff modeling using artificial neural network – a case study of purna sub-catchment of Upper Tapi Basin, India. Environmental Sciences Proceedings, 25 (1), 1-8. https://doi.org/10.3390/ECWS-7-14232
• Moosavi, V., Vafakhah, M., Shirmohammadi, B. & Behnia, N. (2013). A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resources Management, 27, 1301-1321. https://doi.org/10.1007/s11269-012-0239-2
• Nayak, P., Venkatesh, B., Krishna, B. & Jain, S. K. (2013). Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach. Journal of Hydrology, 493, 57-67. https://doi.org/10.1016/j.jhydrol.2013.04.016
• Nourani, V., Baghanam, A. H., Adamowski, J. & Gebremichael, M. (2013). Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. Journal of Hydrology, 476, 228-243. https://doi.org/10.1016/j.jhydrol.2012.10.054
• 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. https://doi.org/10.1016/j.jhydrol.2014.03.057
• Nourani, V., Komasi, M. & Mano, A. A. (2009). Multivariate ANN-Wavelet Approach for Rainfall-Runoff Modeling. Water Resources Management, 23, 2877-2894. https://doi.org/10.1007/s11269-009-9414-5
• Patil, A. P. & Deka, P. C. (2017). Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India. Neural Computing and Applications, 28, 275-285. https://doi.org/10.1007/s00521-015-2055-0
• Raghuwanshi, N. S., Singh, R. & Reddy, L. S. (2006). Runoff and sediment yield modeling using artificial neural networks: Upper Siwane River, India. Journal of Hydrologic Engineering, 11 (1), 71-79. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:1(71)
• Rajaee, T., Nourani, V., Zounemat-Kermani, M. & Kisi, O. (2011). River suspended sediment load prediction: application of ANN and wavelet conjunction model. Journal of Hydrologic Engineering, 16 (8), 613-627. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000347
• Rao, Y. S. & Krishna, B. (2009). Modelling hydrological time series data using wavelet neural network analysis. IAHS publication, 333, 101-111.
• Rezaeian Zadeh, M., Amin, S., Khalili, D. & Singh, V. P. (2010). Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resources Management, 24, 2673-2688. https://doi.org/10.1007/s11269-009-9573-4
• Sarma, J. N. (2005). Fluvial process and morphology of the Brahmaputra River in Assam, India. Geomorphology, 70 (3-4), 226-256. https://doi.org/10.1016/j.geomorph.2005.02.007
• Shafaei, M. & Kisi, O. (2017). Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models. Neural Computing and Applications, 28, 15-28. https://doi.org/10.1007/s00521-016-2293-9
• Sharma, S., Isik, S., Srivastava, P. & Kalin, L. (2013). Deriving spatially distributed precipitation data using the artificial neural network and multilinear regression models. Journal of Hydrologic Engineering, 18 (2), 194-205. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000617
• Shoaib, M., Shamseldin, A. Y., Khan, S., Khan, M. M., Khan, Z. M., Sultan, T. & Melville, B. W. (2018). A comparative study of various hybrid wavelet feedforward neural network models for runoff forecasting. Water Resources Management, 32, 83-103. https://doi.org/10.1007/s11269-017-1796-1
• Solaimani, K. (2009). Rainfall-runoff prediction based on artificial neural network (a case study: Jarahi watershed). American-Eurasian Journal of Agricultural & Environmental Sciences, 5 (6), 856-865.
• Sudheer, K. P., Gosain, A. K. & Ramasastri, K. S. (2002). A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models. Hydrological Processes, 16 (6), 1325-1330. https://doi.org/10.1002/hyp.554
• Sun, Y., Niu, J. & Sivakumar, B. (2019). A comparative study of models for short-term streamflow forecasting with emphasis on wavelet-based approach. Stochastic Environmental Research and Risk Assessment, 33, 1875-1891. https://doi.org/10.1007/s00477-019-01734-7
• Tayfur, G. & Singh, V. P. (2006). ANN and fuzzy logic models for simulating event-based rainfall-runoff. Journal of Hydraulic Engineering, 132 (12), 1321-1330. https://doi.org/10.1061/(ASCE)0733-9429(2006)132:12(1321)
• Wang, K., Band, S. S., Ameri, R., Biyari, M., Hai, T., Hsu, C. C., Hadjouni, M., Elmannai, H., Chau, K. W. & Mosavi, A. (2022). Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow. Engineering Applications of Computational Fluid Mechanics, 16 (1), 1833-1848. https://doi.org/10.1080/19942060.2022.2119281
• Zhang, Z., Zhang, Q. & Sing, V. P. (2018). Univariate streamflow forecasting using commonly used data-driven models: literature review and case study. Hydrological Sciences Journal, 63 (7), 1091-1111. https://doi.org/10.1080/02626667.2018.1469756