한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제53권6호
  • /
  • Pages.395-408
  • /
  • 2020
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
권호 표지
DOI QR코드

DOI QR Code

유출예측을 위한 진화적 기계학습 접근법의 구현: 알제리 세이보스 하천의 사례연구

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)
  • 투고 : 2020.01.15
  • 심사 : 2020.03.29
  • 발행 : 2020.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구논문은 북부아프리카의 알제리에 위치한 하천유역에서 다중선행일 유출량의 예측을 위하여 진화적 최적화기법과 k-fold 교차검증을 결합한 세 개의 서로 다른 기계학습 접근법 (인공신경망, 적응 뉴로퍼지 시스템, 그리고 웨이블릿 기반 신경망)을 개발하고 적용하는 것이다. 인공신경망과 적응 뉴로퍼지 시스템은 root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), 그리고 peak flow criteria (PFC) 의 네 개의 통계지표를 기반으로 하여 모형의 훈련 및 테스팅 결과 유사한 모형수행결과를 나타내었다. 웨이블릿 기반 신경망모형은 하루선행일 테스팅의 결과 RMSE = 8.590 ㎥/sec 과 PFC = 0.252로 분석되어서 인공신경망의 RMSE = 19.120 ㎥/sec, PFC = 0.446 과 적응 뉴로퍼지 시스템의 RMSE = 18.520 ㎥/sec, PFC = 0.444 보다 양호한 결과를 나타내었고, NSE와 R의 값도 웨이블릿 기반 신경망모형이 우수한 것으로 나타났다. 그러므로 웨이블릿 기반 신경망은 알제리 세이보스 하천에서 다중선행일의 예측을 위하여 효율적인 도구로 사용할 수 있다.

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.

키워드

참고문헌

  1. 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. https://doi.org/10.1007/s11269-017-1782-7
  2. 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. https://doi.org/10.1002/1099-1085(20000815/30)14:11/12<2157::AID-HYP57>3.0.CO;2-S
  3. 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. https://doi.org/10.1016/j.jhydrol.2010.06.033
  4. 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. https://doi.org/10.1016/j.egypro.2015.07.832
  5. 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. https://doi.org/10.1016/j.neucom.2013.05.023
  6. 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. https://doi.org/10.1016/j.jhydrol.2013.10.017
  7. 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. https://doi.org/10.30955/gnj.001226
  8. 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. https://doi.org/10.1016/j.aqpro.2015.02.133
  9. 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. https://doi.org/10.1623/hysj.48.3.349.45288
  10. 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. https://doi.org/10.1016/j.agrformet.2019.107647
  11. 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.
  12. Günther, F., and Fritsch, S. (2010). "Neuralnet: Training of neural networks." The R journal, Vol. 2, No. 1, pp. 30-38. https://doi.org/10.32614/RJ-2010-006
  13. 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. https://doi.org/10.1016/j.eswa.2011.04.114
  14. Holland, J.H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, M.I., U.S.
  15. 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. https://doi.org/10.1016/S0022-1694(00)00228-6
  16. 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. https://doi.org/10.1109/21.256541
  17. 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.
  18. 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. https://doi.org/10.1007/s11269-014-0873-y
  19. 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. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000894
  20. 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. https://doi.org/10.1623/hysj.51.4.588
  21. 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. https://doi.org/10.1016/j.jhydrol.2007.12.014
  22. 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. https://doi.org/10.1007/s12205-011-1004-4
  23. 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. https://doi.org/10.4236/jwarp.2011.31006
  24. 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. https://doi.org/10.1016/S0022-1694(00)00322-X
  25. 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. https://doi.org/10.3390/w6061642
  26. 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. https://doi.org/10.1016/j.jhydrol.2006.05.007
  27. 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. https://doi.org/10.1109/34.192463
  28. 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. https://doi.org/10.1016/S0020-7373(75)80002-2
  29. 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. https://doi.org/10.4236/ojmh.2011.11001
  30. 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. https://doi.org/10.1007/s11269-012-0239-2
  31. 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. https://doi.org/10.1016/j.jhydrol.2004.03.027
  32. 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. https://doi.org/10.1016/0022-1694(70)90255-6
  33. 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. https://doi.org/10.1016/j.jhydrol.2007.10.060
  34. 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. https://doi.org/10.1016/j.engappai.2008.09.003
  35. 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. https://doi.org/10.1016/j.jhydrol.2014.03.057
  36. 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. https://doi.org/10.1016/j.jhydrol.2012.10.054
  37. Partal, T. (2009). "Modelling evapotranspiration using discrete wavelet transform and neural networks." Hydrological Processes, Vol. 23, No. 25, pp. 3545-3555. https://doi.org/10.1002/hyp.7448
  38. Partal, T., and Kisi, O. (2007). "Wavelet and neuro-fuzzy conjunction model for precipitation forecasting." Journal of Hydrology, Vol. 342, pp. 199-212. https://doi.org/10.1016/j.jhydrol.2007.05.026
  39. 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. https://doi.org/10.1016/j.jhydrol.2018.05.003
  40. 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. https://doi.org/10.1016/j.jhydrol.2017.04.018
  41. 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. https://doi.org/10.1016/j.jhydrol.2019.03.046
  42. 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. https://doi.org/10.1007/s11269-013-0446-5
  43. 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. https://doi.org/10.5194/hess-15-1835-2011
  44. 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. https://doi.org/10.1016/j.jhydrol.2014.11.050
  45. 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. https://doi.org/10.3390/atmos9070251
  46. 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. https://doi.org/10.1016/j.jhydrol.2014.04.055
  47. 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. https://doi.org/10.1080/02626660109492805
  48. 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. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
  49. 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. https://doi.org/10.1109/TSMC.1985.6313399
  50. 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. https://doi.org/10.1016/j.jhydrol.2010.07.023
  51. 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. https://doi.org/10.1016/j.jhydrol.2010.10.001
  52. 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. https://doi.org/10.1080/19443994.2015.1085908
  53. 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. https://doi.org/10.1080/02723646.2018.1429245
  54. 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. https://doi.org/10.1007/s11600-019-00380-5
  55. 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. https://doi.org/10.1016/S0169-2070(97)00044-7
  56. 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. https://doi.org/10.1016/j.inpa.2018.04.002