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http://dx.doi.org/10.3741/JKWRA.2020.53.6.395

Implementation on the evolutionary machine learning approaches for streamflow forecasting: case study in the Seybous River, Algeria  

Zakhrouf, Mousaab (URMER Laboratory, Faculty of Technology, Department of Hydraulics, University of Tlemcen)
Bouchelkia, Hamid (URMER Laboratory, Faculty of Technology, Department of Hydraulics, University of Tlemcen)
Stamboul, Madani (Research Laboratory of Water Resources, Soil and Environment, Faculty of Architecture and Civil Engineering, Department of Civil Engineering, Amar Telidji University)
Kim, Sungwon (Department of Railroad Construction and Safety Engineering, Dongyang University)
Singh, Vijay P. (Water Engineering, Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University)
Publication Information
Journal of Korea Water Resources Association / v.53, no.6, 2020 , pp. 395-408 More about this Journal
Abstract
This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.
Keywords
Multi-step-days streamflow forecasting; Artificial neural networks; Adaptive neuro-fuzzy inference systems; Wavelet-based neural networks; Genetic algorithm; K-fold cross validation;
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1 Jang, J.S.R. (1993). "ANFIS: Adaptive-network-based fuzzy inference system." IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685.   DOI
2 Jang, J.S.R., Sun, C.T., and Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice-Hall, New Jersey, U.S.
3 Kalteh, A.M. (2015). "Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting." Water Rsources Mnagement, Vol. 29, pp. 1283-1293.   DOI
4 Kamruzzaman, M., Metcalfe, A.V., and Beecham, S. (2013). "Waveletbased rainfall-stream flow models for the southeast Murray Darling basin." Journal of Hydrologic Engineering, Vol. 19, No. 7, pp. 1283-1293.   DOI
5 Keskin, M.E., Taylan, D., and Terzi, O. (2006). "Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series." Hydrological Sciences Journal, Vol. 51, No. 4, pp. 588-598.   DOI
6 Kim, S., and Kim, H.S. (2008). "Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling." Journal of Hydrology, Vol. 351, No. 3, pp. 299-317.   DOI
7 Kisi, O. (2011). "A combined generalized regression neural network wavelet model for monthly streamflow prediction." KSCE Journal of Civil Engineering, Vol. 15, No. 8, pp. 1469-1479.   DOI
8 Krishna, B., Satyaji Rao, Y.R., and Nayak, P.C. (2011). "Time series modeling of river flow using wavelet neural networks." Journal of Water Resource and Protection, Vol. 3, pp. 50-59.   DOI
9 Labat, D., Ababou, R., and Mangin, A. (2000). "Rainfall-runoff relations for karstic springs. Part II: Continuous wavelet and discrete orthogonal multiresolution analyses." Journal of Hydrology, Vol. 238, pp. 149-178.   DOI
10 Liu, W.C., and Chung, C.E. (2014). "Enhancing the predicting accuracy of the water stage using a physical-based model and an artificial neural network-genetic algorithm in a river system." Water, Vol. 6, No. 6, pp. 1642-1661.   DOI
11 Martins, O.Y., Sadeeq, M.A., and Ahaneku, I.E. (2011). "ARMA modelling of Benue river flow dynamics: Comparative study of PAR model." Open Journal of Modern Hydrology, Vol. 1, pp. 1-9.   DOI
12 Lohani, A.K., Goel, N.K., and Bhatia, K.K.S. (2006). "Takagi-Sugeno fuzzy inference system for modeling stage-discharge relationship." Journal of Hydrology, Vol. 331, pp. 146-160.   DOI
13 Mallat, S.G. (1989). "A theory for multiresolution signal decomposition: The wavelet representation." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 674-693.   DOI
14 Mamdani, E.H., and Assilian, S. (1975). "An experiment in linguistic synthesis with a fuzzy logic controller." International Journal of Man-machine Studies, Vol. 7, No. 1, pp. 1-13.   DOI
15 Moosavi, V., Vafakhah, M., Shirmohammadi, B., and Behnia, N. (2013). "A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods." Water Resources Management, Vol. 27, pp. 1301-1321.   DOI
16 Moradkhani, H., Hsu, K.L., Gupta, H.V., and Sorooshian, S. (2004). "Improved streamflow forecasting using self-organizing radial basis function artificial neural networks." Journal of Hydrology, Vol. 295, No. 1, pp. 246-262.   DOI
17 Nash, J.E., and Sutcliffe, J.V. (1970). "River flow forecasting through conceptual models I: A discussion of principles." Journal of Hydrology, Vol. 10, pp. 282-290.   DOI
18 Nasr, A., and Bruen, M. (2008). "Development of neuro-fuzzy models to account for temporal and spatial variations in a lumped rainfall-runoff model." Journal of Hydrology, Vol. 349, pp. 277-290.   DOI
19 Nourani, V., Baghanam, A.H., Adamowski, J., and Kisi, O. (2014). "Applications of hybrid wavelet-artificial intelligence models in hydrology: A review." Journal of Hydrology, Vol. 514, pp. 358-377.   DOI
20 Nourani, V., Alami, M.T., and Aminfar, M.H. (2009). "A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation." Engineering Applications of Artificial Intelligence, Vol. 22, No. 3, pp. 466-472.   DOI
21 Nourani, V., Hosseini, B.A., Adamowski, J., and Gebremicheal, 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, Vol. 476, pp. 228-243.   DOI
22 Partal, T. (2009). "Modelling evapotranspiration using discrete wavelet transform and neural networks." Hydrological Processes, Vol. 23, No. 25, pp. 3545-3555.   DOI
23 Partal, T., and Kisi, O. (2007). "Wavelet and neuro-fuzzy conjunction model for precipitation forecasting." Journal of Hydrology, Vol. 342, pp. 199-212.   DOI
24 Quilty, J., and Adamowski, J. (2018). "Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework." Journal of Hydrology, Vol. 563, pp. 336-353.   DOI
25 Ravansalar, M., Rajaee, T., and Kisi, O. (2017). "Wavelet-linear genetic programming: A new approach for modeling monthly streamflow." Journal of Hydrology, Vol. 549, pp. 461-475.   DOI
26 Rezaie-Balf, M., Kim, S., Fallah, H., and Alaghmand, S. (2019). "Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea." Journal of Hydrology, Vol. 572, pp. 470-485.   DOI
27 Seo, Y., Kim, S., and Singh, V. (2018). "Machine learning models coupled with variational mode decomposition: A new approach for modeling daily rainfall-runoff." Atmosphere, Vol. 9, No. 7, p. 251.   DOI
28 Sahay, R.R., and Srivastava, A. (2014). "Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network." Water Resources Management, Vol. 28, pp. 301-317.   DOI
29 Samsudin, R., Saad, P., and Shabri, A. (2011). "River flow time series using least squares support vector machines." Hydrology and Earth System Sciences, Vol. 15, pp. 1835-1852.   DOI
30 Seo, Y., Kim, S., Kisi, O., and Singh, V.P. (2015). "Daily water level forecasting using wavelet decomposition and artificial intelligence techniques." Journal of Hydrology, Vol. 520, pp. 224-243.   DOI
31 Shoaib, M., Shamseldin, A,Y., and Melville, B,W. (2014). "Comparative study of different wavelet based neural network models for rainfall-runoff modeling." Journal of Hydrology, Vol. 515, pp. 47-58.   DOI
32 Sivakumar, B., Berndtsson, R., Olsson, J., and Jinno, K. (2001). "Evidence of chaos in the rainfall- runoff process." Hydrological Sciences Journal, Vol. 46, No. 1, pp. 131-145.   DOI
33 Stone, M. (1974). "Cross-validatory choice and assessment of statistical predictions." Journal of the royal statistical society. Series B (Methodological), Vol. 36, No. 2, pp. 111-147.   DOI
34 Takagi, T., and Sugeno, M. (1985). "Fuzzy identification of systems and its applications to modeling and control." IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 1, pp. 116-132.   DOI
35 Zhao, X., Guo, X., Luo, J., and Tan, X. (2018). "Efficient detection method for foreign fibers in cotton." Information Processing in Agriculture, Vol. 5, No. 3, pp. 320-328.   DOI
36 Talei, A., Chye, C.L.H., and Wong, T.S.W. (2010). "Evaluation of rainfall and discharge inputs used by adaptive network-based Fuzzy Inference Systems (ANFIS) in rainfall-runoff modeling." Journal of Hydrology, Vol. 391, pp. 248-262.   DOI
37 Zakhrouf, M., Bouchelkia, H., and Stamboul, M. (2016). "Neurowavelet (WNN) and neuro-fuzzy (ANFIS) systems for modeling hydrological time series in arid areas. A case study: The catchment of Aïn Hadjadj (Algeria)." Desalination and Water Treatment, Vol. 57, No. 37, pp. 17182-17194.   DOI
38 Zakhrouf, M., Bouchelkia, H., Stamboul, M., Kim, S., and 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, Vol. 39, No. 6, pp. 506-522.   DOI
39 Zakhrouf, M., Bouchelkia, H., Stamboul, M., and Kim, S. (2020). "Novel hybrid approaches based on evolutionary strategy for streamflow forecasting in the Chellif River, Algeria." Acta Geophysica, Vol. 68, No. 1, pp. 167-180.   DOI
40 Zhang, G., Patuwo, B.E., and Hu, M.Y. (1998). "Forecasting with artificial neural networks: The state of the art." International Journal of Forecasting, Vol. 14, No. 1, pp. 35-62.   DOI
41 Cigizoglu, H.K. (2003). "Estimation, forecasting and extrapolation of river flows by artificial neural networks." Hydrological Sciences Journal, Vol. 48, No. 3, pp. 349-361.   DOI
42 Tiwari, M.K., and Chatterjee, C. (2010). "Development of an accurate and reliable hourly flood forecasting model using waveletbootstrap-ANN (WBANN) hybrid approach." Journal of Hydrology, Vol. 394, pp. 458-470.   DOI
43 Abdollahi, S., Raeisi, J., Khalilianpour, M., Ahmadi, F., and Kisi, O. (2017). "Daily mean streamflow prediction in perennial and non-perennial rivers using four data driven techniques." Water Resources Management, Vol. 31, No. 15, pp. 4855-4874.   DOI
44 Abrahart, R.J., and See, L. (2000). "Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments." Hydrological Processes, Vol. 14, No. 11‐12, pp. 2157-2172.   DOI
45 Adamowski, J., and 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, Vol. 390, No. 1, pp. 85-91.   DOI
46 Aichouri, I., Hani A., Bougherira, N., Djabri, L., Chaffai, H., and Lallahem, S. (2015). "River flow model using artificial neural networks." Energy Procedia, Vol. 74, pp. 1007-1014.   DOI
47 Asadi, S., Shahrabi, J., Abbaszadeh, P., and Tabanmehr, S. (2013). "A new hybrid artificial neural networks for rainfall-runoff process modeling." Neurocomputing, Vol. 121, pp. 470-480.   DOI
48 Badrzadeh, H., Sarukkalige, R., and Jayawardena, A.W. (2013). "Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting." Journal of Hydrology, Vol. 507, pp. 75-85.   DOI
49 Biswas, R.K., and Jayawardena, A.W. (2014). "Water level prediction by artificial neural network in a flashy transboundary river of Bangladesh." Global Nest Journal, Vol. 16, No. 2, pp. 432-444.   DOI
50 Chandwani, V., Vyas, S.K., Agrawal, V., and Sharma, G. (2015). "Soft computing approach for rainfall-runoff modelling: A review." Aquatic Procedia, Vol. 4, No. 2015, pp. 1054-1061.   DOI
51 Guo, J., Zhou, J., Qin, H., Zou, Q., and Li, Q. (2011). "Monthly streamflow forecasting based on improved support vector machine model." Expert Systems with Applications, Vol. 38, No. 10, pp. 13073-13081.   DOI
52 Ghaemi, A., Rezaie-Balf, M., Adamowski, J., Kisi, O., and Quilty, J. (2019). "On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction." Agricultural and Forest Meteorology, Vol. 278, p. 107647.   DOI
53 Gowda, C.C., and Mayya, S.G. (2014). "Comparison of back propagation neural network and genetic algorithm neural network for stream flow prediction." Journal of Computational Environmental Sciences, Vol. 2014, p. 290127.
54 Günther, F., and Fritsch, S. (2010). "Neuralnet: Training of neural networks." The R journal, Vol. 2, No. 1, pp. 30-38.   DOI
55 Holland, J.H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, M.I., U.S.
56 Imrie, C.E., Durucan, S., and Korre, A. (2000). "River flow prediction using artificial neural networks: Generalisation beyond the calibration range." Journal of Hydrology, Vol. 233, No. 1, pp. 138-153.   DOI