Abstract
The main aim of this study was to develop hybrid machine learning (ML)-based ensemble modeling of the rainfall-runoff process in the Katar catchment, Ethiopia. This study used four single ML models, namely the general regression neural network (GRNN), long short-term memory neural network (LSTM), extreme learning machine (ELM) and Hammerstein-Weiner (HW) for modeling the rainfall-runoff process. Subsequently, two strategies were followed to improve the performance of the single models. In the first strategy, simple average ensemble (SAE), weighted average ensemble (WAE), Hammerstein-Weiner ensemble (HWE) and Neuro-fuzzy ensemble (NFE) were developed using the results of the single models. A hybrid Boosted Regression Tree (BRT) ensemble was developed in the second strategy to enhance the single models’ modeling accuracy. The study used ten years (2008–2017) of data for calibration and validation of the developed models. The performances of the developed models were assessed using root mean square error (RMSE), percent bias (PBIAS), mean absolute error (MAE) and Nash-Sutcliffe coefficient efficiency (NSE). The results of single ML models showed that the LSTM model gave the best prediction performance with NSE = 0.933 and RMSE = 5.308 m3/s in the validation phase. For ensemble modeling, the best result was obtained by NFE increasing the performance of HW, GRNN, LSTM and ELM models by 3.35%, 13.25%, 2.57% and 19.9%, respectively. Evaluation of the hybrid BRT models showed that all the hybrid models provide reliable modeling performance with LSTM-BRT demonstrating better predictive accuracy (NSE = 0.981, RMSE = 1.999 m3/s and PBIAS = 0.75%). In general, the result of this study proved the promising influence of ensemble techniques and hybrid BRT models for rainfall-runoff modeling.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Please contact author for data requests.
References
Abba SI, Linh NTT, Abdullahi J, Ali SIA, Pham QB, Abdulkadir RA, Costache R, Nam VT, Anh DT (2020) Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration. IEEE Access 8:157218–157237. https://doi.org/10.1109/ACCESS.2020.3017743
Abedi R, Costache R, Shafizadeh-Moghadam H, Pham QB (2022) Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int 37(19):5479–5496. https://doi.org/10.1080/10106049.2021.1920636
Abinayadhevi, P, Prasad, SJS (2015) Identification of pH process using Hammerstein-Wiener model. Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015, 1–5. https://doi.org/10.1109/ISCO.2015.7282297
Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577(July):123981. https://doi.org/10.1016/j.jhydrol.2019.123981
Adnan RM, Petroselli A, Heddam S, Santos CAG, Kisi O (2021) Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stoch Env Res Risk A 35(3):597–616. https://doi.org/10.1007/s00477-020-01910-0
Alilou VK, Yaghmaee F (2015) Application of GRNN neural network in non-texture image inpainting and restoration. Pattern Recogn Lett 62:24–31. https://doi.org/10.1016/j.patrec.2015.04.020
Asadi H, Shahedi K, Jarihani B, Sidle R (2019) Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach. Water 11(2):212. https://doi.org/10.3390/w11020212
Bengio Y, Simard P, Frasconi P (1994) Learning Long-Term Dependencies with Gradient Descent is Difficult. IEEE Trans Neural Netw 5(2):157–166. https://doi.org/10.1109/72.279181
Bhattacharjee NV, Tollner EW (2016) Improving management of windrow composting systems by modeling runoff water quality dynamics using recurrent neural network. Ecol Model 339:68–76. https://doi.org/10.1016/j.ecolmodel.2016.08.011
Cai, QC, Hsu, TH, Lin, JY (2021) Using the general regression neural network method to calibrate the parameters of a sub-catchment. Water (Switzerland), 13(8). https://doi.org/10.3390/w13081089
Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77(4):802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
Elkiran G, Nourani V, Abba S (2019) Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. J Hydrol 577(June):123962. https://doi.org/10.1016/j.jhydrol.2019.123962
Gaya MS, Zango MU, Yusuf LA, Mustapha M, Muhammad B, Sani A, Tijjani A, Wahab NA, Khairi MTM (2017) Estimation of turbidity in water treatment plant using hammerstein-wiener and neural network technique. Indon J Electric Eng Comput Sci 5(3):666–672. https://doi.org/10.11591/ijeecs.v5.i3.pp666-672
Gers FA, Schmidhuber J, Cummins F (2000) Learning to Forget: Continual Prediction with LSTM. Neural Comput 12(10):2451–2471
Hadi SJ, Abba SI, Sammen SSH, Salih SQ, Al-Ansari N, Yaseen MZ (2019) Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation. IEEE Access 7:141533–141548. https://doi.org/10.1109/ACCESS.2019.2943515
Harmel RD, Smith PK, Migliaccio KW, Chaubey I, Douglas-Mankin KR, Benham B, Shukla S, Muñoz-Carpena R, Robson BJ (2014) Evaluating, interpreting, and communicating performance of hydrologic/water quality models considering intended use: A review and recommendations. Environ Model Softw 57:40–51. https://doi.org/10.1016/j.envsoft.2014.02.013
Heddam S (2014) Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA. Environ Monit Assess 186(11):7837–7848. https://doi.org/10.1007/s10661-014-3971-7
Himanshu SK, Pandey A, Yadav B (2017) Ensemble wavelet-support vector machine approach for prediction of suspended sediment load using hydrometeorological data. J Hydrol Eng 22(7):05017006
Jang JSR (1993) ANFIS : Adap tive-Ne twork-Based Fuzzy Inference System. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
Ji X, Shang X, Dahlgren RA, Zhang M (2017) Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River. China Environ Sci Pollut Res 24(19):16062–16076. https://doi.org/10.1007/s11356-017-9243-7
Jimeno-Sáez, P, Senent-Aparicio, J, Pérez-Sánchez, J, Pulido-Velazquez, D (2018) A comparison of SWAT and ANN models for daily runoff simulation in different climatic zones of peninsular Spain. Water (Switzerland), 10(2). https://doi.org/10.3390/w10020192
Kaveh K, Kaveh H, Bui MD, Rutschmann P (2021) Long short-term memory for predicting daily suspended sediment concentration. Eng Comput 37(3):2013–2027. https://doi.org/10.1007/s00366-019-00921-y
Khan MYA, Hasan F, Tian F (2019) Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin. India Sustain Water Resource Manag 5(3):1115–1131. https://doi.org/10.1007/s40899-018-0288-7
Kiran RN, Ravi V (2008) Software reliability prediction by soft computing techniques. J Syst Softw 81(4):576–583. https://doi.org/10.1016/j.jss.2007.05.005
Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58. https://doi.org/10.1016/j.jhydrol.2012.05.031
Kisi O, Shiri J, Tombul M (2013) Modeling rainfall-runoff process using soft computing techniques. Comput Geosci 51:108–117. https://doi.org/10.1016/j.cageo.2012.07.001
Koch J, Schneider R (2022) Long short-term memory networks enhance rainfall-runoff modelling at the national scale of Denmark. GEUS Bull 49:1–7. https://doi.org/10.34194/geusb.v49.8292
Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrol Earth Syst Sci 22(11):6005–6022. https://doi.org/10.5194/hess-22-6005-2018
Lakmini Prarthana Jayasinghe WJM, Deo RC, Ghahramani A, Ghimire S, Raj N (2022) Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data. J Hydrol 607(December 2021):127534. https://doi.org/10.1016/j.jhydrol.2022.127534
Li X, Sha J, Wang Z (2019) Comparison of daily streamflow forecasts using extreme learning machines and the random forest method. Hydrol Sci J 64(15):1857–1866. https://doi.org/10.1080/02626667.2019.1680846
Liu P, Wang J, Sangaiah AK, Xie Y, Yin X (2019) Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment. Sustain 11(2058):1–14. https://doi.org/10.3390/su11072058
Malik A, Jamei M, Ali M, Prasad R, Karbasi M, Yaseen ZM (2022) Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection. Agric Water Manag 272(March):107812. https://doi.org/10.1016/j.agwat.2022.107812
Mehr AD, Kahya E, Şahin A, Nazemosadat MJ (2015) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol 12(7):2191–2200. https://doi.org/10.1007/s13762-014-0613-0
Mohammadi, B, Safari, MJS, Vazifehkhah, S (2022) IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling. In Scientific Reports (Vol. 12, Issue 1). https://doi.org/10.1038/s41598-022-16215-1
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153
Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: Performance measures and evaluation criteria. Trans ASABE 58(6):1763–1785. https://doi.org/10.13031/trans.58.10715
Mundher Z, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-shafie A (2016) Stream-flow forecasting using extreme learning machines : A case study in a semi-arid region in Iraq. J Hydrol 542:603–614. https://doi.org/10.1016/j.jhydrol.2016.09.035
Mundher Z, Mohammed Y, Allawi F, Yousif AA, Jaafar O, Mohamad F, Ahmed H (2018) Non-tuned machine learning approach for hydrological time series forecasting. Neural Comput & Applic 30(5):1479–1491. https://doi.org/10.1007/s00521-016-2763-0
Niu W, Feng Z, Feng B, Min Y, Cheng C (2019a) Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir. Water 11(1):88. https://doi.org/10.3390/w11010088
Niu W, Feng Z, Zeng M, Feng B, Min Y (2019b) Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm. Appl Soft Comput J 82:105589. https://doi.org/10.1016/j.asoc.2019.105589
Noori N, Kalin L (2016) Coupling SWAT and ANN models for enhanced daily streamflow prediction. J Hydrol 533:141–151. https://doi.org/10.1016/j.jhydrol.2015.11.050
Nourani V, Elkiran G, Abba SI (2018) Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach. Water Sci Technol 78(10):2064–2076. https://doi.org/10.2166/wst.2018.477
Nourani V, Elkiran G, Abdullahi J (2019) Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. J Hydrol 577(June):123958. https://doi.org/10.1016/j.jhydrol.2019.123958
Nourani V, Elkiran G, Abdullahi J (2020a) Multi-step ahead modeling of reference evapotranspiration using a multi-model approach. J Hydrol 581(October 2019):124434. https://doi.org/10.1016/j.jhydrol.2019.124434
Nourani V, Gökçekuş H, Umar IK (2020b) Artificial intelligence based ensemble model for prediction of vehicular traffic noise. Environ Res 180(October 2019):108852. https://doi.org/10.1016/j.envres.2019.108852
Nourani, V, Gokcekus, H, Gelete, G (2021a) Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model. Complexity, 2021(Article ID 6633760), 19. https://doi.org/10.1155/2021/6633760
Nourani V, Gökçekuş H, Gichamo T (2021b) Ensemble data-driven rainfall-runoff modeling using multi-source satellite and gauge rainfall data input fusion. Earth Sci Inf 14(4):1787–1808. https://doi.org/10.1007/s12145-021-00615-4
Nourani V, Khodkar K, Gebremichael M (2022) Uncertainty assessment of LSTM based groundwater level predictions. Hydrol Sci J 67(5):773–790. https://doi.org/10.1080/02626667.2022.2046755
Park, S, Kim, J (2019) Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl Sci (Switzerland), 9(5). https://doi.org/10.3390/app9050942
Pfannerstill M, Guse B, Fohrer N (2014) Smart low flow signature metrics for an improved overall performance evaluation of hydrological models. J Hydrol 510:447–458. https://doi.org/10.1016/j.jhydrol.2013.12.044
Pham QB, Abba SI, Usman AG, Linh NTT, Gupta V, Malik A, Costache R, Vo ND, Tri DQ (2019) Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall. Water Resour Manag 33(15):5067–5087. https://doi.org/10.1007/s11269-019-02408-3
Phukoetphim P, Shamseldin, Asaad Y, Adams, Keith (2016) Multimodel Approach Using Neural Networks and Symbolic Regression to Combine the Estimated Discharges of Rainfall-Runoff Models. J Hydrol Eng 21(8):1–18. https://doi.org/10.1061/(ASCE)HE.1943-5584
Rahimzad, M, Moghaddam Nia, A, Zolfonoon, H, Soltani, J, Danandeh Mehr, A, Kwon, HH (2021) Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting. In Water Resources Management (Vol. 35, Issue 12, pp. 4167–4187). https://doi.org/10.1007/s11269-021-02937-w
Sharafati A, Seyed H, Asadollah SB, Motta D, Yaseen ZM (2020) Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrol Sci J 65(12):1–21. https://doi.org/10.1080/02626667.2020.1786571
Sharghi E, Nourani V, Behfar N (2018) Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. J Hydroinf 20(5):1071–1084. https://doi.org/10.2166/hydro.2018.151
Taormina R, Chau K (2015) Data-driven input variable selection for rainfall – runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines. J Hydrol 529:1617–1632. https://doi.org/10.1016/j.jhydrol.2015.08.022
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(D7):7183–7192. https://doi.org/10.1029/2000JD900719
Tibangayuka N, Mulungu DMM, Izdori F (2022) Evaluating the performance of HBV, HEC-HMS and ANN models in simulating streamflow for a data scarce high-humid tropical catchment in Tanzania. Hydrol Sci J 67(14):1–14. https://doi.org/10.1080/02626667.2022.2137417
Umar IK, Nourani V, Gökçekuş H (2021) A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration. Environ Sci Pollut Res 28(36):49663–49677. https://doi.org/10.1007/s11356-021-14133-9
Xiang Z, Yan J, Demir I (2020) A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning. Water Resour Res 56(1):1–17. https://doi.org/10.1029/2019WR025326
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. J Hydrol 598(October 2020):126378. https://doi.org/10.1016/j.jhydrol.2021.126378
Young CC, Liu WC, Wu MC (2017) A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Appl Soft Comput J 53:205–216. https://doi.org/10.1016/j.asoc.2016.12.052
Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region. Saudi Arabia Landslides 13(5):839–856. https://doi.org/10.1007/s10346-015-0614-1
Yun D, Abbas A, Jeon J, Ligaray M, Baek SS, Cho KH (2021) Developing a deep learning model for the simulation of micro-pollutants in a watershed. J Clean Prod 300:126858. https://doi.org/10.1016/j.jclepro.2021.126858
Zhang D, Skullestad E, Lindholm G, Ratnaweera H (2018) Hydraulic modeling and deep learning based flow forecasting for optimizing inter catchment wastewater transfer. J Hydrol 567:792–802. https://doi.org/10.1016/j.jhydrol.2017.11.029
Zhang X, Zwiers FW, Li G, Wan H, Cannon AJ (2017) Complexity in estimating past and future extreme short-duration rainfall. Nat Geosci 10(4):255–259. https://doi.org/10.1038/ngeo2911
Funding
This research received no external funding.
Author information
Authors and Affiliations
Contributions
Data processing, conceptualization, modeling and writing-up of the paper were conducted by Gebre Gelete,
Corresponding author
Ethics declarations
Consent for publication
Not applicable.
Competing interests
The authors declare there is no conflict.
Additional information
Communicated by: H. Babaie
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gelete, G. Application of hybrid machine learning-based ensemble techniques for rainfall-runoff modeling. Earth Sci Inform 16, 2475–2495 (2023). https://doi.org/10.1007/s12145-023-01041-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-01041-4