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Integrating Support Vector Machines with Different Ensemble Learners for Improving Streamflow Simulation in an Ungauged Watershed

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Abstract

Streamflow simulation, particularly in ungauged watersheds, poses a significant challenge in surface water hydrology. The estimation of natural river and streamflow has been a research focus in recent years, with numerous strategies proposed. Hybrid ensemble soft computing models have proven their effectiveness in predicting flow rates. This study proposes a modeling approach that integrates a support vector machine (SVM) with several ensemble learning techniques, such as Bagging, Dagging, Random subspace, and Rotation Forest, to predict flow rates in natural rivers of a Mediterranean climate in Algeria. The gauging data of the hydrometric station “Amont des gorges” were used, and the following quantitative parameters were considered: flow, velocity, depth, width, and hydraulic radius. The proposed models were evaluated based on Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and correlation coefficient (R). Our results indicated that the ensemble models outperformed the standalone SVM model. More specifically, the SVM-Dagging model performed the best, with RMSE = 6.58, NSE = 0.76 and R = 0.96, followed by SVM-Bagging (RMSE = 6.83, NSE = 0.75, and R = 0.96), SVM-RF (RMSE = 6.95, NSE = 0.74, and R = 0.95), SVM-RSS (RMSE = 8.34, NSE = 0.62, and R = 0.93), and the standalone SVM models (RMSE = 7.71, NSE = 0.68, and R = 0.88), respectively. These findings suggest that the proposed ensemble models are valuable tools for accurately forecasting stream and river flows, aiding planners and decision-makers. Accurate prediction of flow rates in natural rivers can enhance water resource planning, optimize resource allocation, and improve water management practices.

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References

  • Adnan RM, Jaafari A, Mohanavelu A, Kisi O, Elbeltagi A (2021) Novel ensemble forecasting of streamflow using locally weighted learning algorithm. Sustainability 13(11):5877

    Article  Google Scholar 

  • Adnan Ikram R, Khan I, Moayedi H, Dehrashid AA, Elkhrachy I, Le BN (2023) Novel evolutionary-optimized neural network for predicting landslide susceptibility. Environ Dev Sustain 1–33

  • Atashi V, Barati R, Lim YH (2023) Improved river flood routing with spatially variable exponent muskingum model and sine cosine optimization algorithm. Environ Process 10(42):1–20

  • Blöschl G, Hall J, Viglione A, Perdigão RA, Parajka J, Merz B et al (2019) Changing climate both increases and decreases European river floods. Nature 573(7772):108–111

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Article  Google Scholar 

  • Chen Sh, Zhang H, Zykova KI, Gholizadeh Touchaei H, Yuan C, Moayedi H, Le BN (2023) Computational intelligence models for predicting the frictional resistance of driven pile foundations in cold regions. Comput Concr 32(2):217–232

    Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Article  Google Scholar 

  • Gao C, Hao M, Chen J, Gu C (2021) Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region. China Urban Clim 40:101005

    Article  Google Scholar 

  • Gong S, Bai X, Luo G, Li C, Wu L, Chen F, Zhang S (2023) Climate change has enhanced the positive contribution of rock weathering to the major ions in riverine transport. Glob Planet Change 228:104203

    Article  Google Scholar 

  • Guo Y, Zhang Y, Zhang L, Wang Z (2021) Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review. Wiley Interdiscip Rev Water 8(1):e1487

    Article  Google Scholar 

  • Hafied Y, Marouf N, Bouziane MT, Remini B, Lubomir S (2019) Load sediments quantification in Algerian north-west basins by ANN (artificial neurons network) method. Geosci Eng 65(3):1–17

    Article  Google Scholar 

  • He M, Dong J, Jin Z, Liu C, Xiao J, Zhang F, Deng L (2021) Pedogenic processes in loess-paleosol sediments: Clues from Li isotopes of leachate in Luochuan loess. Geochim Cosmochim Acta 299:151–162

    Article  Google Scholar 

  • Ibrahim KSMH, Huang YF, Ahmed AN, Koo CH, El-Shafie A (2022) A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alex Eng J 61(1):279–303

    Article  Google Scholar 

  • Kuncheva L. I Rodríguez, J. J (2007) An experimental study on rotation forest ensembles. In Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23–25 (2007) Proceedings 7. Springer, Berlin Heidelberg, pp 459–468

    Google Scholar 

  • Li J, Wang Z, Wu X, Xu C, Guo S, Chen X (2020) Toward Monitoring Short-Term Droughts Using a Novel Daily Scale, Standardized Antecedent Precipitation Evapotranspiration Index. J Hydrometeorol 21(5):891–908

    Article  Google Scholar 

  • Li Q, Lu L, Zhao Q, Hu S (2023) Impact of Inorganic Solutes’ Release in Groundwater during Oil Shale In Situ Exploitation. Water 15(1):172

    Article  Google Scholar 

  • Luo J, Niu F, Lin Z, Liu M, Yin G, Gao Z (2022) Abrupt increase in thermokarst lakes on the central Tibetan Plateau over the last 50 years. Catena 217:106497

    Article  Google Scholar 

  • Ma S, Qiu H, Yang D, Wang J, Zhu Y, Tang B, Cao M (2023) Surface multi-hazard effect of underground coal mining. Landslides 20(1):39–52

    Article  Google Scholar 

  • Marouf N (2012) Study of water quality and sediment transport in the Beni-Haroun dam (MILA): Its impact on the environment of the region. Mohamed Khider University, Biskra, Algeria, p 242

    Google Scholar 

  • Marouf N, Remini B (2011) Temporal variability in sediment concentration and hysteresis in the Wadi Kebir Rhumel Basin of Algeria. HKIE Transactions 18(1):13–21

    Article  Google Scholar 

  • Meddi M, Toumi S, Assani AA (2017) Application of the L-moments approach to the analysis of regional flood frequency in Northern Algeria. Int J Hydrol Sci Technol 7(1):77–102

    Article  Google Scholar 

  • Moayedi H, Dehrashid AA (2023) A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping. Environ Sci Pollut Res 30:82964–82989

    Article  Google Scholar 

  • Moayedi H, Varamini N, Mosallanezhad M, Foong LK, Le BN (2022) Applicability and comparison of four nature-inspired hybrid techniques in predicting driven piles’ friction capacity. Transp Geotech 37:100875

    Article  Google Scholar 

  • Moayedi H, Salari M, Dehrashid AA, Le BN (2023) Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain. Stoch Environ Res Risk Ass 37:2961–2976

    Article  Google Scholar 

  • Moramarco T, Singh VP (2001) Simple method for relating local stage and remote discharge. J Hydrol Eng 6(1):78–81

    Article  Google Scholar 

  • Muhammetoglu A, Orhan P, Akdegirmen O, Dugan ST, Muhammetoglu H (2023) An Integrated Modeling Approach to Assess Best Management Practices (BMPs) for Improving Stream Water Quality Using the MapShed and WASP8 Models. Water Resour Manage 10:1–7

    Google Scholar 

  • Nhu VH, Janizadeh S, Avand M, Chen W, Farzin M, Omidvar E et al (2020) GIS-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models. Appl Sci 10(6):2039

    Article  Google Scholar 

  • Ni L, Wang D, Singh VP, Wu J, Wang Y, Tao Y, Zhang J (2020) Streamflow and rainfall forecasting by two long short-term memory-based models. J Hydrol 583:124296

    Article  Google Scholar 

  • Pham BT, Jaafari A, Van Phong T, Yen HPH, Tuyen TT, Van Luong V et al (2021) Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosci Front 12(3):101105

    Article  Google Scholar 

  • Pham BT, Nguyen-Thoi T, Qi C, Van Phong T, Dou J, Ho LS et al (2020) Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena 195:104805

  • Qiu D, Zhu G, Bhat MA, Wang L, Liu Y, Sang L, Sun N (2023) Water use strategy of nitraria tangutorum shrubs in ecological water delivery area of the lower inland river: Based on stable isotope data. J Hydrol 624:129918

    Article  Google Scholar 

  • Rhomad H, Khalil K, Elkalay K (2023) Water Quality Modeling in Atlantic Region: Review, Science Mapping and Future Research Directions. Water Resour Manage 37(1):451–499

    Article  Google Scholar 

  • Rui S, Zhou Z, Jostad HP, Wang L, Guo Z (2023) Numerical prediction of potential 3-dimensional seabed trench profiles considering complex motions of mooring line. Appl Ocean Res 139:103704

    Article  Google Scholar 

  • Saha S, Kundu B, Paul GC, Pradhan B (2023) Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models. Stoch Environ Res Risk Assess 1–28

  • Sahoo A, Samantaray S, Ghose DK (2019) Stream flow forecasting in Mahanadi River Basin using artificial neural networks. Procedia Comput Sci 157:168–174

    Article  Google Scholar 

  • Samantaray S, Sahoo A, Ghose DK (2020) Assessment of sediment load concentration using SVM, SVM-FFA and PSR-SVM-FFA in arid watershed, India: a case study. J Civ Eng 24:1944–1957

    Google Scholar 

  • Samantaray S, Sahoo A, Agnihotri A (2023) Prediction of Flood Discharge Using Hybrid PSO-SVM Algorithm in Barak River Basin. MethodsX 10:102060

    Article  Google Scholar 

  • Swain JB, Patra KC (2017) Streamflow estimation in ungauged catchments using regionalization techniques. J Hydrol 554:420–433

    Article  Google Scholar 

  • Tao H, Al-Sulttani AO, Salih Ameen AM, Ali ZH, Al-Ansari N, Salih SQ, Mostafa RR (2020) Training and testing data division influence on hybrid machine learning model process: application of river flow forecasting. Complexity 8844367:1–22

    Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres 106(D7):7183–7192

    Article  Google Scholar 

  • Ting KM, Witten IH (1997) Stacking bagged and dagged models. In: Proceeding ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning. 367–375. ISBN:1-55860-486-3

  • Tran QC, Minh DD, Jaafari A, Al-Ansari N, Minh DD, Van DT et al (2020) Novel ensemble landslide predictive models based on the Hyperpipes algorithm: a case study in the Nam Dam Commune. Vietnam Appl Sci 10(11):3710

    Article  Google Scholar 

  • Tuyen TT, Jaafari A, Yen HPH, Nguyen-Thoi T, Van Phong T, Nguyen HD et al (2021) Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm. Ecol Inform 63:101292

    Article  Google Scholar 

  • Wang X, Wang T, Xu J, Shen Z, Yang Y, Chen A, Piao S (2022) Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion. Nat Ecol Evol 6(7):890–899

    Article  Google Scholar 

  • Yadav B, Gupta PK, Patidar N, Himanshu SK (2020) Ensemble modelling framework for groundwater level prediction in urban areas of India. Sci Tot Environ 712:135539

    Article  Google Scholar 

  • Yaseen ZM, Ebtehaj I, Bonakdari H, Deo RC, Mehr AD, Mohtar WH, Diop L, El-Shafie A, Singh VP (2017) Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. J Hydrol 554:263–276

    Article  Google Scholar 

  • Yaseen ZM, Sulaiman SO, Deo RC, Chau KW (2019) An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408

    Article  Google Scholar 

  • Yin L, Wang L, Li T, Lu S, Tian J, Yin Z, Zheng W (2023) U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model. Land 12(10):1859

  • Zhao Y, Gor M, Voronkova DK, Gholizadeh Touchaei H, Moayedi H, Le BN (2023) An optimized ANFIS model for predicting pile pullout resistance. Comput Concr 48(2):179–190

    Google Scholar 

  • Zhou G, Yang Z (2023) Analysis for 3-D morphology structural changes for underwater topographical in Culebrita Island. Int J Remote Sens 44(7):2458–2479

    Article  Google Scholar 

  • Zhou G, Wu G, Zhou X, Xu C, Zhao D, Lin J, Zhang L (2023) Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data. Int J Appl Earth Obs Geoinf 118:103253

    Google Scholar 

  • Zhu G, Liu Y, Shi P, Jia W, Zhou J, Liu Y, Zhao K (2022) Stable water isotope monitoring network of different water bodies in Shiyang River basin, a typical arid river in China. Earth Syst Sci Data 14(8):3773–3789

    Article  Google Scholar 

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Hydraulic, Faculty of Sciences and Applied Sciences, University of Larbi-Ben-M’hidi, Oum-El-Bouaghi, 04000, Algeria

    Yahi Takai Eddine, Marouf Nadir & Sehtal Sabah

  2. Laboratory of Natural Resources and Development of Sensitive Environments, Laboratory of Functional Ecology and Environment, University of Larbi-Ben-M’hidi, Oum-El-Bouaghi, 04000, Algeria

    Yahi Takai Eddine & Marouf Nadir

  3. Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, 1496793612, Iran

    Abolfazl Jaafari

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  1. Yahi Takai Eddine
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  2. Marouf Nadir
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  4. Abolfazl Jaafari
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Contributions

Conceptualization, Yahi Takai Eddine, Marouf Nadir, and Sehtal Sabah; Data acquisition, Yahi Takai Eddine, Marouf Nadir, and Sehtal Sabah; Methodology, Abolfazl Jaafari; Writing-original draft preparation, Yahi Takai Eddine, Marouf Nadir, Sehtal Sabah, Abolfazl Jaafari; Writing-review and editing, Abolfazl Jaafari.

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Correspondence to Abolfazl Jaafari.

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Takai Eddine, Y., Nadir, M., Sabah, S. et al. Integrating Support Vector Machines with Different Ensemble Learners for Improving Streamflow Simulation in an Ungauged Watershed. Water Resour Manage 38, 553–567 (2024). https://doi.org/10.1007/s11269-023-03684-w

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