Water for future (물과 미래)

Korea Water Resources Association (한국수자원학회)
권호 표지

데이터 기반 모형을 활용한 도시 유출량 예측

  • ;
  • 김선호 (세종대학교 건설환경공학과) ;
  • 배덕효 (세종대학교 건설환경공학과)
  • Published : 2022.03.31
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References

  1. Adnan, R.M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., & Li, B. (2019). Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, 124371. https://doi.org/10.1016/j.jhydrol.2019.124371
  2. Apaydin, H., & Sibtain, M. (2021). A multivariate streamflow forecasting model by integrating improved complete ensemble empirical mode decomposition with additive noise, sample entropy, Gini index and sequence-to-sequence approaches. Journal of Hydrology, 603, 126831. https://doi.org/10.1016/j.jhydrol.2021.126831
  3. Berkhahn, S., Fuchs, L., & Neuweiler, I. (2019). An ensemble neural network model for real-time prediction of urban floods. Journal of Hydrology, 575, 743-754. https://doi.org/10.1016/j.jhydrol.2019.05.066
  4. Carranza, C., Nolet, C., Pezij, M., & van der Ploeg, M. (2021). Root zone soil moisture estimation with Random Forest. Journal of Hydrology, 593, 125840. https://doi.org/10.1016/j.jhydrol.2020.125840
  5. Chipman, H.A., George, E.I., & McCulloch, R.E. (2012). BART: Bayesian additive regression trees. Annals of Applied Statistics, 6, 266-298.
  6. Desai, S., & Ouarda, T.B.M.J. (2021). Regional hydrological frequency analysis at ungauged sites with random forest regression. Journal of Hydrology, 594, 125861. https://doi.org/10.1016/j.jhydrol.2020.125861
  7. Dodangeh, E., Panahi, M., Rezaie, F., Lee, S., Tien Bui, D., Lee, C.W., & Pradhan, B. (2020). Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search. Journal of Hydrology, 590, 125423. https://doi.org/10.1016/j.jhydrol.2020.125423
  8. Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K.W., Ardabili, S.F., & Piran, M.J. (2018). Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics, 12, 411-437. https://doi.org/10.1080/19942060.2018.1448896
  9. Freire, P.K. de M.M., Santos, C.A.G., & Silva, G.B.L. da (2019). Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting, Applied Soft Computing Journal 80, 494-505. https://doi.org/10.1016/j.asoc.2019.04.024
  10. Gharaei-Manesh, S., Fathzadeh, A., & Taghizadeh-Mehrjardi, R. (2016). Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran. Cold Reg., Sci. Technol. 122, 26-35. https://doi.org/10.1016/j.coldregions.2015.11.004
  11. Ibrahim, H., & Karakurt, O. (2013). Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. Journal of Hydrology, 477, 119-128. https://doi.org/10.1016/j.jhydrol.2012.11.015
  12. Kansanen, K., Vauhkonen, J., Lahivaara, T., Seppanen, A., Maltamo, M., & Mehtatalo, L. (2019). Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching. ISPRS J. Photogramm, Remote Sensing. 152, 66-78. https://doi.org/10.1016/j.isprsjprs.2019.04.007
  13. Kim, T., Shin, J.Y., Kim, S., & Heo, J.H. (2018). Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition. Journal of Hydrology, 557, 726-739. https://doi.org/10.1016/j.jhydrol.2017.12.069
  14. Lee, E., & Kim, S. (2020). Characterization of runoff generation in a mountainous hillslope according to multiple threshold behavior and hysteretic loop features. Journal of Hydrology, 590, 125534. https://doi.org/10.1016/j.jhydrol.2020.125534
  15. Luat, N.-V., Shin, J., & Lee, K. (2020). Hybrid BART-based models optimized by nature-inspired metaheuristics to predict ultimate axial capacity of CCFST columns, Engineering with Computers. https://doi.org/10.1007/s00366-008-0118-x
  16. Luo, X., Yuan, X., Zhu, S., Xu, Z., Meng, L., & Peng, J. (2019). A hybrid support vector regression framework for streamflow forecast. Journal of Hydrology, 568, 184-193. https://doi.org/10.1016/j.jhydrol.2018.10.064
  17. Mosavi, A., Ozturk, P., & Chau, K. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10, 1536. https://doi.org/10.3390/w10111536
  18. Nguyen, D.H., & Bae, D.H. (2020). Correcting mean areal precipitation forecasts to improve urban flooding predictions by using long short-term memory network. Journal of Hydrology, 584, 124710. https://doi.org/10.1016/j.jhydrol.2020.124710
  19. Nguyen, D.H., Hien Le, X., Heo, J.Y., & Bae, D.H. (2021). Development of an Extreme Gradient Boosting Model Integrated with Evolutionary Algorithms for Hourly Water Level Prediction. IEEE Access 9, 125853-125867. https://doi.org/10.1109/ACCESS.2021.3111287
  20. Nguyen, D.H., Le, X.H., Anh, D.T., Kim, S.H., & Bae, D.H. (2022). Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm. Journal of Hydrology, 606, 127445. https://doi.org/10.1016/j.jhydrol.2022.127445
  21. Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J., Liu, J. (2020). Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model. Journal of Hydrology, 586, 124901. https://doi.org/10.1016/j.jhydrol.2020.124901
  22. Prasad, R., Deo, R.C., Li, Y., & Maraseni, T. (2017). Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm, Atmospheric Research, 197, 42-63. https://doi.org/10.1016/j.atmosres.2017.06.014
  23. Ren, K., Fang, W., Qu, J., Zhang, X., & Shi, X. (2020). Comparison of eight filter-based feature selection methods for monthly streamflow forecasting - Three case studies on CAMELS data sets. Journal of Hydrology, 586, 124897. https://doi.org/10.1016/j.jhydrol.2020.124897
  24. Salmasi, F., & Abraham, J. (2021). Prediction of discharge coefficients for sluice gates equipped with different geometric sills under the gate using multiple non-linear regression (MNLR), Journal of Hydrology, 597, 125728. https://doi.org/10.1016/j.jhydrol.2020.125728
  25. Scrucca, L., 2013. GA: A package for genetic algorithms in R, Journal of Statistical Software, 53, 1-37. https://doi.org/10.18637/jss.v053.i04
  26. Sevinc, V., Kucuk, O., & Goltas, M. (2020). A Bayesian network model for prediction and analysis of possible forest fire causes, Forest Ecology and Management, 457, 117723. https://doi.org/10.1016/j.foreco.2019.117723
  27. Tan, Y.V., & Roy, J. (2019). Bayesian additive regression trees and the General BART model, Statistics in Medicine, 38, 5048-5069. https://doi.org/10.1002/sim.8347
  28. Taormina, R., & Chau, K.-W. (2015). ANN-Based Interval Forecasting of Streamflow Discharges Using the LUBE Method and MOFIPS, Engineering Applications of Artificial Intelligence, 45, 429-440. https://doi.org/10.1016/j.engappai.2015.07.019
  29. Wang, X., Kingsland, G., Poudel, D., & Fenech, A. (2019). Urban flood prediction under heavy precipitation. Journal of Hydrology, 577, 123984. https://doi.org/10.1016/j.jhydrol.2019.123984
  30. Yaseen, Z.M., Ebtehaj, I., Bonakdari, H., Deo, R.C., Danandeh Mehr, A., Mohtar, W.H.M.W., Diop, L., El-shafie, A., & Singh, V.P. (2017). Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. Journal of Hydrology, 554, 263-276. https://doi.org/10.1016/j.jhydrol.2017.09.007
  31. Yin, H., Zhang, X., Wang, F., Zhang, Y., Xia, R., & Jin, J. (2021). Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model. Journal of Hydrology, 598, 126378. https://doi.org/10.1016/j.jhydrol.2021.126378
  32. Zamanian, S., Terranova, B., & Shafieezadeh, A. (2020). Significant variables affecting the performance of concrete panels impacted by wind-borne projectiles: A global sensitivity analysis. International Journal of Impact Engineering, 144, 103650. https://doi.org/10.1016/j.ijimpeng.2020.103650
  33. Zhang, T., & Geem, Z.W. (2019). Review of harmony search with respect to algorithm structure, Swarm and Evolutionary Computation, 48, 31-43. https://doi.org/10.1016/j.swevo.2019.03.012
  34. Zhang, T., Geng, G., Liu, Y., & Chang, H.H. (2020). Application of bayesian additive regression trees for estimating daily concentrations of pm2.5 components, Atmosphere (Basel). 11.
  35. Zhou, Y., Guo, S., & Chang, F.J. (2019). Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts. Journal of Hydrology, 570, 343-355. https://doi.org/10.1016/j.jhydrol.2018.12.040