Machine learning models coupled with empirical mode decomposition for simulating monthly and yearly streamflows: a case study of three watersheds in Ontario, Canada

References

• Achite, M., Jehanzaib, M., Elshaboury, N., & Kim, T. W. (2022). Evaluation of machine learning techniques for hydrological drought modeling: A case study of the Wadi Ouahrane Basin in Algeria. Water (Switzerland), 14(3), 431. https://doi.org/10.3390/w14030431
   Google Scholar
• Ahmadpour, H., Bazrafshan, O., Rafiei-Sardooi, E., Zamani, H., & Panagopoulos, T. (2021). Gully erosion susceptibility assessment in the Kondoran watershed using machine learning algorithms and the Boruta feature selection. Sustainability, 13(18), 10110. https://doi.org/10.3390/su131810110
   Web of Science ®Google Scholar
• Bafitlhile, T. M., & Li, Z. (2019). Applicability of ϵ-support vector machine and artificial neural network for flood forecasting in humid, semi-humid and semi-arid basins in China. Water, 11(1), 85. https://doi.org/10.3390/w11010085
   Web of Science ®Google Scholar
• Bahamonde, P. A., Fuzzen, M. L., Bennett, C. J., Tetreault, G. R., McMaster, M. E., Servos, M. R., Martyniuk, C. J., & Munkittrick, K. R. (2015). Whole organism responses and intersex severity in rainbow darter (Etheostoma caeruleum) following exposures to municipal wastewater in the Grand River basin, ON, Canada. Part A. Aquatic Toxicology, 159, 290–301. https://doi.org/10.1016/j.aquatox.2014.11.023
   PubMed Web of Science ®Google Scholar
• Band, S. S., Heggy, E., Bateni, S. M., Karami, H., Rabiee, M., Samadianfard, S., Chau, K. W., & Mosavi, A. (2021). Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics, 15(1), 1147–1158. https://doi.org/10.1080/19942060.2021.1944913
   Web of Science ®Google Scholar
• Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. https://doi.org/10.1016/j.mlwa.2021.100204
   Google Scholar
• Bartoletti, N., Casagli, F., Marsili-Libelli, S., Nardi, A., & Palandri, L. (2018). Data-driven rainfall/runoff modelling based on a neuro-fuzzy inference system. Environmental Modelling & Software, 106, 35–47. https://doi.org/10.1016/j.envsoft.2017.11.026
   Web of Science ®Google Scholar
• Barzegar, R., Aalami, M. T., & Adamowski, J. (2021). Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting. Journal of Hydrology, 598, 126196. https://doi.org/10.1016/j.jhydrol.2021.126196
   Web of Science ®Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/bf00058655
   Web of Science ®Google Scholar
• Chen, X., Li, F. W., & Feng, P. (2018). A new hybrid model for nonlinear and non-stationary runoff prediction at annual and monthly time scales. Journal of Hydro-Environment Research, 20, 77–92. https://doi.org/10.1016/j.jher.2018.05.004
   Web of Science ®Google Scholar
• Chollet, F. (2017, January). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (pp. 1800–1807). https://doi.org/10.1109/CVPR.2017.195
   Google Scholar
• Chu, T. Y., & Huang, W. C. (2020). Application of empirical mode decomposition method to synthesize flow data: A case study of Hushan Reservoir in Taiwan. Water, 12(4), 927. https://doi.org/10.3390/W12040927
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
   Web of Science ®Google Scholar
• Cui, Z., Qing, X., Chai, H., Yang, S., Zhu, Y., & Wang, F. (2021). Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis. Journal of Hydrology, 603, 127124. https://doi.org/10.1016/j.jhydrol.2021.127124
   Web of Science ®Google Scholar
• Elbeltagi, A., Di Nunno, F., Kushwaha, N. L., de Marinis, G., & Granata, F. (2022). River flow rate prediction in the Des Moines watershed (Iowa, USA): a machine learning approach (Iowa, USA): A machine learning approach. Stochastic Environmental Research and Risk Assessment, 36(11), 3835–3855. https://doi.org/10.1007/s00477-022-02228-9
   Web of Science ®Google Scholar
• Eslami, E., Salman, A. K., Choi, Y., Sayeed, A., & Lops, Y. (2020). A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks. Neural Computing and Applications, 32(11), 7563–7579. https://doi.org/10.1007/s00521-019-04287-6
   Web of Science ®Google Scholar
• Farhana, N., Firdaus, A., Darmawan, M. F., & Ab Razak, M. F. (2023). Evaluation of Boruta algorithm in DDoS detection. Egyptian Informatics Journal, 24, 27–42. https://doi.org/10.1016/j.eij.2022.10.005
   Web of Science ®Google Scholar
• Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/s10994-006-6226-1
   Web of Science ®Google Scholar
• Ghimire, S., Yaseen, Z. M., Farooque, A. A., Deo, R. C., Zhang, J., & Tao, X. (2021). Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks. Scientific Reports, 11(1), 17497. https://doi.org/10.1038/s41598-021-96751-4
   PubMed Web of Science ®Google Scholar
• Gizaw, M. S., & Gan, T. Y. (2016). Regional flood frequency analysis using support vector regression under historical and future climate. Journal of Hydrology, 538, 387–398. https://doi.org/10.1016/j.jhydrol.2016.04.041
   Web of Science ®Google Scholar
• Goliatt, L., Sulaiman, S. O., Khedher, K. M., Farooque, A. A., & Yaseen, Z. M. (2021). Estimation of natural streams longitudinal dispersion coefficient using hybrid evolutionary machine learning model. Engineering Applications of Computational Fluid Mechanics, 15, 1298–1320. https://doi.org/10.1080/19942060.2021.1972043
   Web of Science ®Google Scholar
• Golshan, M., Kavian, A., Esmali, A., & Ziegler, A. D. (2020). Runoff and sediment yield modeling in data-sparse catchments in the Garehsoo River basin, northern Iran. Environmental Earth Sciences, 79(14), 351. https://doi.org/10.1007/s12665-020-09084-2
   Web of Science ®Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016, December). Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90.
   Google Scholar
• He, X., Luo, J., Zuo, G., & Xie, J. (2019). Daily runoff forecasting using a hybrid model based on variational mode decomposition and deep neural networks. Water Resources Management, 33(4), 1571–1590. https://doi.org/10.1007/s11269-019-2183-x
   Web of Science ®Google Scholar
• Ho, E., Tsuji, L. J. S., & Gough, W. A. (2005). Trends in river-ice break-up data for the western James Bay region of Canada. Polar Geography, 29(4), 291–299. https://doi.org/10.1080/789610144
   Google Scholar
• Htike, K. K. (2017). Efficient determination of the number of weak learners in AdaBoost. Journal of Experimental & Theoretical Artificial Intelligence, 29(5), 967–982. https://doi.org/10.1080/0952813X.2016.1266038
   Web of Science ®Google Scholar
• Hu, C., Wu, Q., Li, H., Jian, S., Li, N., & Lou, Z. (2018). Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water (Switzerland), 10(11), 1543. https://doi.org/10.3390/w10111543
   Google Scholar
• Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Yen, N., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
   Web of Science ®Google Scholar
• Hussain, D., Hussain, T., Khan, A. A., Naqvi, S. A. A., & Jamil, A. (2020). A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin. Earth Science Informatics, 13(3), 915–927. https://doi.org/10.1007/s12145-020-00477-2
   Web of Science ®Google Scholar
• Idris, A., Khan, A., & Lee, Y. S. (2012). Genetic Programming and Adaboosting based churn prediction for Telecom. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1328–1332. https://doi.org/10.1109/ICSMC.2012.6377917
   Google Scholar
• Jamei, M., Ali, M., Karimi, B., Karbasi, M., Farooque, A. A., & Yaseen, Z. M. (2023). Surface water electrical conductivity and bicarbonate ion determination using a smart hybridization of optimal Boruta package with Elman recurrent neural network. Process Safety and Environmental Protection, 174, 115–134. https://doi.org/10.1016/j.psep.2023.03.062
   Web of Science ®Google Scholar
• Jose, D. M., Vincent, A. M., & Dwarakish, G. S. (2022). Management of validation of HPLC method for determination of acetylsalicylic acid impurities in a new pharmaceutical product. Scientific Reports, 12(1), 1–25. https://doi.org/10.1038/s41598-021-99269-x
   PubMed Web of Science ®Google Scholar
• Kalteh, A. M. (2013). Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Computers & Geosciences, 54, 1–8. https://doi.org/10.1016/j.cageo.2012.11.015
   Web of Science ®Google Scholar
• Kamath, P. R., & Senapati, K. (2021). Short-term wind speed forecasting using S-transform with compactly supported kernel. Wind Energy, 24(3), 260–274. https://doi.org/10.1002/we.2571
   Web of Science ®Google Scholar
• Karthikeyan, L., & Nagesh Kumar, D. (2013). Predictability of nonstationary time series using wavelet and EMD based ARMA models. Journal of Hydrology, 502, 103–119. https://doi.org/10.1016/j.jhydrol.2013.08.030
   Web of Science ®Google Scholar
• Kolachian, R., & Saghafian, B. (2021). Hydrological drought class early warning using support vector machines and rough sets. Environmental Earth Sciences, 80(11), 390. https://doi.org/10.1007/s12665-021-09536-3
   Web of Science ®Google Scholar
• Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall-runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005–6022. https://doi.org/10.5194/hess-22-6005-2018
   Web of Science ®Google Scholar
• Krause, P., Smith, A., Veale, B., & Murray, M. (2001). Achievements of the grand river conservation authority, Ontario, Canada. Water Science and Technology, 43(9), 45–55. https://doi.org/10.2166/wst.2001.0506
   PubMed Web of Science ®Google Scholar
• Kumar, P., Prasad, R., Choudhary, A., Gupta, D. K., Mishra, V. N., Vishwakarma, A. K., Singh, A. K., & Srivastava, P. K. (2019). Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data. Geocarto International, 34(9), 1022–1041. https://doi.org/10.1080/10106049.2018.1464601
   Web of Science ®Google Scholar
• Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36), https://doi.org/10.18637/jss.v036.i11
   PubMed Web of Science ®Google Scholar
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
   Web of Science ®Google Scholar
• Lee, T., & Ouarda, T. B. M. J. (2012). Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition. Water Resources Research, 48(2), https://doi.org/10.1029/2011WR010660
   Google Scholar
• Liu, S., Wang, J., Wang, H., & Wu, Y. (2022). Post-processing of hydrological model simulations using the convolutional neural network and support vector regression. Hydrology Research, 53(4), 605–621. https://doi.org/10.2166/nh.2022.004
   Web of Science ®Google Scholar
• Liu, S., Xu, J., Zhao, J., Xie, X., & Zhang, W. (2014). Efficiency enhancement of a process-based rainfall-runoff model using a new modified AdaBoost.RT technique. Applied Soft Computing, 23, 521–529. https://doi.org/10.1016/j.asoc.2014.05.033
   Web of Science ®Google Scholar
• Luo, C., Zhang, X., Wang, Y., Men, Z., & Liu, H. (2022). Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine. Soil and Tillage Research, 219, 105325. https://doi.org/10.1016/j.still.2022.105325
   Web of Science ®Google Scholar
• Maguire, T., Manuel, L., Smedinga, R. A., & Biehl, M. (2022). A review of feature selection and ranking methods. In R. Smedinga & M. Biehl (Eds.), Proceedings of the 19th SC@RUG 2022 Proceedings 2021–2022 (pp. 15–20). Groningen: Rijksuniversiteit Groningen. https://pure.rug.nl/ws/portalfiles/portal/214074117/proceedings_2022.pdf
   Google Scholar
• Mallick, J., Talukdar, S., & Ahmed, M. (2022). Combining high resolution input and stacking ensemble machine learning algorithms for developing robust groundwater potentiality models in Bisha watershed, Saudi Arabia. Applied Water Science, 12(4), 77. https://doi.org/10.1007/s13201-022-01599-2
   Web of Science ®Google Scholar
• Meddage, D. P. P., Ekanayake, I. U., Weerasuriya, A. U., & Lewangamage, C. S. (2021). Tree-based regression models for predicting external wind pressure of a building with an unconventional configuration. In Proceedings of MERCon 2021-7th International Multidisciplinary Moratuwa Engineering Research Conference (pp. 257–262). https://doi.org/10.1109/MERCon52712.2021.9525734
   Google Scholar
• Meng, E., Huang, S., Huang, Q., Fang, W., Wu, L., & Wang, L. (2019). A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. Journal of Hydrology, 568, 462–478. https://doi.org/10.1016/j.jhydrol.2018.11.015
   Web of Science ®Google Scholar
• Metzger, R. A., Doherty, J. F., Jenkins, D. M., & Hall, D. L. (2020). Approximate entropy and empirical mode decomposition for improved speaker recognition. Advances in Data Science and Adaptive Analysis, 12(03n04), 2050011. https://doi.org/10.1142/S2424922X20500114
   Google Scholar
• Mohammadi, B. (2021). A review on the applications of machine learning for runoff modeling. Sustainable Water Resources Management, 7(6), 98. https://doi.org/10.1007/s40899-021-00584-y
   Web of Science ®Google Scholar
• Mozaffari, S., Javadi, S., Moghaddam, H. K., & Randhir, T. O. (2022). Forecasting groundwater levels using a hybrid of support vector regression and particle swarm optimization. Water Resources Management, 36(6), 1955–1972. https://doi.org/10.1007/s11269-022-03118-z
   Web of Science ®Google Scholar
• Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. B, 4, 51–62. https://doi.org/10.20544/HORIZONS.B.04.1.17.P05
   Google Scholar
• Parisouj, P., Mohebzadeh, H., & Lee, T. (2020). Employing machine learning algorithms for streamflow prediction: A case study of four river basins with different climatic zones in the United States. Water Resources Management, 34(13), 4113–4131. https://doi.org/10.1007/s11269-020-02659-5
   Web of Science ®Google Scholar
• Parisouj, P., Mokari, E., Mohebzadeh, H., Goharnejad, H., Jun, C., Oh, J., & Bateni, S. M. (2022). Physics-informed data-driven model for predicting streamflow: A case study of the Voshmgir Basin, Iran. Applied Sciences, 12(15), 7464. https://doi.org/10.3390/app12157464
   Google Scholar
• Pekárová, P., Miklánek, P., & Pekár, J. (2003). Spatial and temporal runoff oscillation analysis of the main rivers of the world during the 19th-20th centuries. Journal of Hydrology, 274(1-4), 62–79. https://doi.org/10.1016/S0022-1694(02)00397-9
   Web of Science ®Google Scholar
• Prasad, R., Ali, M., Kwan, P., & Khan, H. (2019). Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Applied Energy, 236, 778–792. https://doi.org/10.1016/j.apenergy.2018.12.034
   Web of Science ®Google Scholar
• Sadeghi, M., Asanjan, A. A., Faridzad, M., Nguyen, P. H. U., Hsu, K., Sorooshian, S., & Braithwaite, D. A. N. (2019). PERSIANN-CNN: Precipitation estimation from remotely sensed information using artificial neural networks–convolutional neural networks. Journal of Hydrometeorology, 20(12), 2273–2289. https://doi.org/10.1175/JHM-D-19-0110.1
   Web of Science ®Google Scholar
• Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. WIRES Data Mining and Knowledge Discovery, 8(4), e1249. https://doi.org/10.1002/widm.1249
   Web of Science ®Google Scholar
• Sánchez-Martínez, A., Ruíz-Oropeza, E. Y., Orozco-del-Castillo, M. G., Hernández-Gómez, J. J., & Yáñez-Casas, G. A. (2022). Assessment of the reduction of the icesnow coverage at the TransMexican Volcanic Belt through empirical mode decomposition on satellite imagery. In Advances in geospatial data science: Selected papers from the international conference on geospatial information sciences 2021 (pp. 131–148). Springer International Publishing. https://doi.org/10.1007/978-3-030-98096-2_10.
   Google Scholar
• Shrestha, N. K., & Shukla, S. (2015). Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment. Agricultural and Forest Meteorology, 200, 172–184. https://doi.org/10.1016/j.agrformet.2014.09.025
   Web of Science ®Google Scholar
• Sibtain, M., Li, X., Bashir, H., & Azam, M. I. (2021). A hybrid model for runoff prediction using variational mode decomposition and artificial neural network. Water Resources, 48(5), 701–712. https://doi.org/10.1134/S0097807821050171
   Web of Science ®Google Scholar
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv, 1409–1556. https://arxiv.org/abs/1409.1556.
   Google Scholar
• Singh, U. K., Jamei, M., Karbasi, M., Malik, A., & Pandey, M. (2022). Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture. Journal of Hydrology, 607, 127549. https://doi.org/10.1016/j.jhydrol.2022.127549
   Web of Science ®Google Scholar
• Soltani, K., Ebtehaj, I., Amiri, A., Azari, A., Gharabaghi, B., & Bonakdari, H. (2021). Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of the Total Environment, 770, 145288. https://doi.org/10.1016/j.scitotenv.2021.145288
   PubMed Web of Science ®Google Scholar
• St. George, S. (2007). Streamflow in the Winnipeg River basin, Canada: Trends, extremes and climate linkages. Journal of Hydrology, 332(3-4), 396–411. https://doi.org/10.1016/j.jhydrol.2006.07.014
   Web of Science ®Google Scholar
• Story, A., & Buttle, J. M. (2001). Precipitation data quality and long-term water balances within the Moose River Basin, east-central Canada. Atmosphere-Ocean, 39(1), 55–69. https://doi.org/10.1080/07055900.2001.9649666
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Sun, K., Hu, L., Guo, J., Yang, Z., Zhai, Y., & Zhang, S. (2021). Enhancing the understanding of hydrological responses induced by ecological water replenishment using improved machine learning models: A case study in Yongding River. Science of the Total Environment, 768, 145489. https://doi.org/10.1016/j.scitotenv.2021.145489
   PubMed Web of Science ®Google Scholar
• Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-ResNet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31, https://doi.org/10.1609/aaai.v31i1.11231
   Google Scholar
• Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818–2826). https://doi.org/10.1109/CVPR.2016.308.
   Google Scholar
• Tarfaya, C., Houichi, L., & Heddam, S. (2022). Prediction of index rainfall in ungauged regions of Algeria: Survey of rule-based models using geographic predictors. Arabian Journal of Geosciences, 15(7), 668. https://doi.org/10.1007/s12517-022-09944-0
   Google Scholar
• Tayyab, M., Ahmad, I., Sun, N., Zhou, J., & Dong, X. (2018). Application of integrated artificial neural networks based on decomposition methods to predict streamflow at Upper Indus Basin, Pakistan. Atmosphere, 9(12), 494. https://doi.org/10.3390/atmos9120494
   Web of Science ®Google Scholar
• Tu, T., Ishida, K., Ercan, A., Kiyama, M., Amagasaki, M., & Zhao, T. (2021). Hybrid precipitation downscaling over coastal watersheds in Japan using WRF and CNN. Journal of Hydrology: Regional Studies, 37, 100921. https://doi.org/10.1016/j.ejrh.2021.100921
   Google Scholar
• Tyralis, H., Papacharalampous, G., & Langousis, A. (2021). Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. Neural Computing and Applications, 33(8), 3053–3068. https://doi.org/10.1007/s00521-020-05172-3
   Web of Science ®Google Scholar
• Wang, W.-C., Chau, K.-W., Qiu, L., & Chen, Y.-B. (2015). Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition. Environmental Research, 139, 46–54. https://doi.org/10.1016/j.envres.2015.02.002
   PubMed Web of Science ®Google Scholar
• Weeks, W. D., & Boughton, W. C. (1987). Tests of ARMA model forms for rainfall-runoff modelling. Journal of Hydrology, 91(1-2), 29–47. https://doi.org/10.1016/0022-1694(87)90126-0
   Web of Science ®Google Scholar
• Williams, G. R. (1961). Cyclical variations in world-wide hydrologic data. Journal of the Hydraulics Division, 87(6), 71–88. https://doi.org/10.1061/JYCEAJ.0000668
   Google Scholar
• Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. https://doi.org/10.1016/S0893-6080(05)80023-1
   Web of Science ®Google Scholar
• Yuan, R., Cai, S., Liao, W., Lei, X., Zhang, Y., Yin, Z., Ding, G., Wang, J., & Xu, Y. (2021). Daily runoff forecasting using ensemble empirical mode decomposition and long short-term memory. Frontiers in Earth Science, 9, 621780. https://doi.org/10.3389/feart.2021.621780
   Web of Science ®Google Scholar
• Zhang, C., & Ma, Y. (2012). Ensemble machine learning: Methods and applications. Springer US. https://doi.org/10.1007/978-1-4419-9326-7
   Google Scholar
• Zhang, Y., Zhang, C., Sun, J., & Guo, J. (2018). Improved wind speed prediction using empirical mode decomposition. Advances in Electrical and Computer Engineering, 18(2), 3–10. https://doi.org/10.4316/AECE.2018.02001
   Web of Science ®Google Scholar
• Zhao, Y., Meng, X., Qi, T., Li, Y., Chen, G., Yue, D., & Qing, F. (2022). AI-based rainfall prediction model for debris flows. Engineering Geology, 296, 106456. https://doi.org/10.1016/j.enggeo.2021.106456
   Web of Science ®Google Scholar
• Zhu, J., & Pierskalla, W. P. (2016). Applying a weighted random forests method to extract karst sinkholes from LiDAR data. Journal of Hydrology, 533, 343–352. https://doi.org/10.1016/j.jhydrol.2015.12.012
   Web of Science ®Google Scholar