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Hybrid Extreme Gradient Boosting and Nonlinear Ensemble Models for Suspended Sediment Load Prediction in an Agricultural Catchment

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  • Gebre Gelete

    (Near East University
    Arsi University)

Abstract

In this study, four individual models namely Hammerstein-Weiner (HW), Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM) and Least Square Support Vector Machine (LSSVM) were utilized for modeling the SSL of Katar catchment in Ethiopia. Then, two strategies were applied to improve the overall predictive accuracy. The first strategy involves the development of four ensemble techniques such as simple average ensemble (SE), weighted average ensemble (WE), neuro-fuzzy ensemble (NFE) and HW ensemble (HWE) using the SSL output of individual models. In the second strategy, a hybrid Extreme Gradient Boosting (XGB) model was developed to improve the performance of the base models. The accuracy of the models was evaluated using the percent bias (Pbias), Nash–Sutcliffe coefficient efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE) and determination coefficient (R2). The result showed that the HW model provided the best modeling result among the individual models with NSE and Pbias values of 0.922 and -8.8%, respectively, for the testing period. Among the ensemble techniques, NFE provided the best performance by increasing the NSE values of the individual models by 4.88% to 62.52%, during the testing period. Examination of the hybrid XGB models showed that all hybrid models performed reliably, with the HW-XGB model achieving the best predictive performance (NSE = 0.989). Overall, the results of this study showed the promising potential of ensemble and hybrid XGB models for SSL modeling in an agricultural catchment.

Suggested Citation

  • Gebre Gelete, 2023. "Hybrid Extreme Gradient Boosting and Nonlinear Ensemble Models for Suspended Sediment Load Prediction in an Agricultural Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(14), pages 5759-5787, November.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:14:d:10.1007_s11269-023-03629-3
    DOI: 10.1007/s11269-023-03629-3
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    1. Peiqiang Gao & Wenfeng Du & Qingwen Lei & Juezhi Li & Shuaiji Zhang & Ning Li, 2023. "NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1481-1497, March.
    2. Elham Ghanbari-Adivi & Mohammad Ehteram & Alireza Farrokhi & Zohreh Sheikh Khozani, 2022. "Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4313-4342, September.
    3. Hai Tao & Behrooz Keshtegar & Zaher Mundher Yaseen, 2019. "The Feasibility of Integrative Radial Basis M5Tree Predictive Model for River Suspended Sediment Load Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4471-4490, October.
    4. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3405-3422, August.
    5. Siyamak Doroudi & Ahmad Sharafati & Seyed Hossein Mohajeri & Haitham Afan, 2021. "Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method," Complexity, Hindawi, vol. 2021, pages 1-13, March.
    6. Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    7. Quoc Bao Pham & S. I. Abba & Abdullahi Garba Usman & Nguyen Thi Thuy Linh & Vivek Gupta & Anurag Malik & Romulus Costache & Ngoc Duong Vo & Doan Quang Tri, 2019. "Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5067-5087, December.
    8. Jenq-Tzong Shiau & Ting-Ju Chen, 2015. "Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2805-2818, June.
    9. Hamid Moeeni & Hossein Bonakdari, 2018. "Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 845-863, February.
    10. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
    11. Vahid Nourani & Amir Molajou & Ali Davanlou Tajbakhsh & Hessam Najafi, 2019. "A Wavelet Based Data Mining Technique for Suspended Sediment Load Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1769-1784, March.
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