278
Views
0
CrossRef citations to date
0
Altmetric
Research Article

An extreme forecast index-driven runoff prediction approach using stacking ensemble learning

, , , &
Article: 2353144 | Received 05 Nov 2023, Accepted 04 May 2024, Published online: 10 Jun 2024

References

  • Achite M, Elshaboury N, Jehanzaib M, Vishwakarma D, Pham Q, Anh D, Abdelkader E, Elbeltagi A.,. 2023. Performance of machine learning techniques for meteorological drought forecasting in the Wadi Mina Basin, Algeria. Water. 15(4):765. doi: 10.3390/w15040765.
  • Agarwal A, Singh RD. 2004. Runoff modelling through back propagation artificial neural network with variable rainfall-runoff data. Water Resour Manage. 18:285–300.
  • Akhter T, Pandit BA, Vishwakarma DK, Kumar R, Mishra R, Maryam M.,. 2022. Meteorological drought quantification using standardized precipitation index for Gulmarg area of Jammu and Kashmir. J Soil Water Conserv. 21(3):260–267. doi: 10.5958/2455-7145.2022.00033.9.
  • Bougeault P, Toth Z, Bishop C, et al. 2010. The THORPEX Interactive Grand Global Ensemble. B Am Meteorol Soc. 91(8):1059–1072.
  • Chen X, Parajka J, Széles B, Strauss P, Blöschl G. 2020. Controls on event runoff coefficients and recession coefficients for different runoff generation mechanisms identified by three regression methods. J Hydrol Hydromech. 68(2):155–169. doi: 10.2478/johh-2020-0008.
  • Elbeltagi A, Kumar M, Kushwaha NL, Pande CB, Ditthakit P, Vishwakarma DK, Subeesh A.,. 2023a. Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India. Stoch Environ Res Risk Assess. 37(1):113–131. doi: 10.1007/s00477-022-02277-0.
  • Elbeltagi A, Pande CB, Kumar M, Tolche AD, Singh SK, Kumar A, Vishwakarma DK.,. 2023b. Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models. Environ Sci Pollut Res Int. 30(15):43183–43202. doi: 10.1007/s11356-023-25221-3.
  • Emerton RE, Stephens EM, Pappenberger F, et al. 2016. Continental and global scale flood forecasting systems. Wiley Interdiscip Rev Water. 3(3):391–418.
  • Fernández-Delgado M, Cernadas E, Barro S, et al. 2014. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 15(1):3133–3181.
  • Hosseini SM, Mahjouri N. 2016. Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling. Appl Soft Comput. 38:329–345.
  • Inoue J. 2020. Review of forecast skills for weather and sea ice in supporting Arctic navigation. Polar Sci. 27:100523. doi: 10.1016/j.polar.2020.100523.
  • Kennedy J, Eberhart R. 1995. Particle swarm optimization//Proceedings of ICNN’95-international conference on neural networks. IEEE. 4, p. 1942–1948.
  • Kimuli JB, Di B, Zhang R, et al. 2021. A multisource trend analysis of floods in Asia-Pacific 1990-2018: implications for climate change in sustainable development goals. Int J Disaster Risk Reduct. 59(5):102237.
  • Kitanidis PK, Bras RL. 1980. Real-time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resour Res. 16(6):1034–1044.
  • Kumar D, Singh VK, Abed SA, Tripathi VK, Gupta S, Al-Ansari N, Vishwakarma DK, Dewidar AZ, Al‑Othman AA, Mattar MA, et al. 2023. Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms. Appl Water Sci. 13(10):192. doi: 10.1007/s13201-023-02005-1.
  • Lalaurette F. 2003. Early detection of abnormal weather conditions using a probabilistic extreme forecast index. QJR Meteorol Soc. 129(594):3037–3057.
  • Liang Z, Li Y, Hu Y, Li B, Wang J. 2018. A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework. Theor Appl Climatol. 133(1-2):137–149.,. doi: 10.1007/s00704-017-2186-6.
  • Markuna S, Kumar P, Ali R, et al. 2023. Application of innovative machine learning techniques for long-term rainfall prediction. Pure Appl Geophys. 180(1):335–363.
  • Mirzania E, Vishwakarma DK, Bui Q-AT, Band SS, Dehghani R.,. 2023. A novel hybrid AIG-SVR model for estimating daily reference evapotranspiration. Arab J Geosci. 16(5):1–14. doi: 10.1007/s12517-023-11387-0.
  • Mosavi A, Ozturk P, Chau K. 2018. Flood prediction using machine learning models: literature review. Water. 10(11):1536. doi: 10.3390/w10111536.
  • Pedregosa F. 2016. Hyperparameter optimization with approximate gradient//International conference on machine learning. PMLR. p. 737–746.
  • Prates F, Buizza R. 2011. PRET, the Probability of RETurn: a new probabilistic product based on generalized extreme-value theory. QJR Meteorol Soc. 137(655):521–537.
  • Rajaee T, Khani S, Ravansalar M. 2020. Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: a review. Chemom Intell Lab Syst. 200:103978.
  • Riad S, Mania J, Bouchaou L, et al. 2004. Rainfall-runoff model using an artificial neural network approach. Math Comput Model. 40(7-8):839–846.
  • Saha A, Pal S, Arabameri A, Blaschke T, Panahi S, Chowdhuri I, Chakrabortty R, Costache R, Arora A.,. 2021. Flood susceptibility assessment using novel ensemble of hyperpipes and support vector regression algorithms. Water. 13(2):241. doi: 10.3390/w13020241.
  • Sahoo BB, Jha R, Singh A, Kumar D.,. 2019. Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophys. 67(5):1471–1481. doi: 10.1007/s11600-019-00330-1.
  • Singh AK, Kumar P, Ali R, Al-Ansari N, Vishwakarma DK, Kushwaha KS, Panda KC, Sagar A, Mirzania E, Elbeltagi A, et al. 2022. An integrated statistical-machine learning approach for runoff prediction. Sustainability. 14(13):8209. doi: 10.3390/su14138209.
  • Sudriani Y, Ridwansyah I, Rustini HA. 2019. Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia//IOP Conference Series: earth and Environmental Science. IOP Conf Ser Earth Environ Sci. 299(1):012037. doi: 10.1088/1755-1315/299/1/012037.
  • Sun W, Trevor B. 2018. A stacking ensemble learning framework for annual river ice breakup dates. J Hydrol. 561:636–650.
  • Todini E. 2007. Hydrological catchment modelling: past, present and future. Hydrol Earth Syst Sci. 11(1):468–482.
  • Tsonevsky I, Doswell CA, Brooks HE. 2018. Early warnings of severe convection using the ECMWF extreme forecast index. Weather Forecast. 33(3):857–871. doi: 10.1175/WAF-D-18-0030.1.
  • Vishwakarma DK, Kumar R, Abed SA, Al-Ansari N, Kumar A, Kushwaha NL, Yadav D, Kumawat A, Kuriqi A, Alataway A, et al. 2023b. Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation. Sci Rep. 13(1):14981. doi: 10.1038/s41598-023-41435-4.
  • Vishwakarma DK, Kuriqi A, Abed SA, Kishore G, Al-Ansari N, Pandey K, Kumar P, Kushwaha NL, Jewel A.,. 2023a. Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test. Heliyon. 9(5):e16290. doi: 10.1016/j.heliyon.2023.e16290.
  • Wu Z, Zhou Y, Wang H, Jiang Z.,. 2020. Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse. Sci Total Environ. 716:137077. doi: 10.1016/j.scitotenv.2020.137077.
  • Xiang Y, Gou L, He L, et al. 2018. A SVR-ANN combined model based on ensemble EMD for rainfall prediction. Appl Soft Comput. 73:874–883.
  • Xu Y, Hu C, Wu Q, et al. 2022. Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation. J Hydrol. 608:127553.
  • Yang B, Chen L, Singh VP, Yi B, Leng Z, Zheng J, Song Q. 2023. A method for monthly extreme precipitation forecasting with physical explanations. Water. 15(8):1545. doi: 10.3390/w15081545.
  • Yu X, Liong SY. 2007. Forecasting of hydrologic time series with ridge regression in feature space. J Hydrol. 332(3–4):290–302.
  • Yu X, Wang Y, Wu L, et al. 2020. Comparison of support vector regression and extreme gradient boosting for decomposition-based data-driven 10-day streamflow forecasting. J Hydrol. 582:124293.
  • Zhou Z. 2009. Ensemble learning. Encycl Biometr. 1
  • Zounemat-Kermani M, Batelaan O, Fadaee M, et al. 2021. Ensemble machine learning paradigms in hydrology: a review. J Hydrol. 598:126266.
  • Zsótér E. 2006. Recent developments in extreme weather forecasting. ECMWF Newsl. 107(107):8–17.