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Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models

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Abstract

Accurate streamflow estimation is vital for effective water resources management, including flood mitigation, drought warning, and reservoir operation. This paper aims to evaluate four machine learning (ML) algorithms, namely, Long Short-Term Memory (LSTM), Regression Tree, AdaBoost, and Gradient Boosting algorithms, to predict the futuristic streamflow of the Swat River basin. Ten General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSPs) 245 and 585 were used for futuristic streamflow assessment. The ML models were developed using maximum temperature, minimum temperature, and precipitation as the input variables while streamflow as the target variable. The performance of ML models was assessed via statistical performance indicators, namely the coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). The AdaBoost exhibits exceptional performance (R2: 0.99 during training, 0.86 during testing). The futuristic streamflow projection shows an increase in mean annual streamflow between 2050 and 2080 s from 3.26 to 7.52% for SSP245 and 3.77–13.55% for SSP585. ML models, notably adaboost, provide a reliable method for projecting streamflow, will assist in hazard and water management in the area.

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References

  • Adib MNM, and Sobri Harun (2022) Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting. J Hydrol Eng 27:05022004

    Article  Google Scholar 

  • Almazroui M, Saeed S, Saeed F, Islam MN, Muhammad Ismail (2020) Projections of precipitation and temperature over the south Asian countries in CMIP6. Earth SystemsEnvironment 4:297–320

    Google Scholar 

  • Breiman L, Jerome H, Friedman RA, Olshen, Stone CJ (1984) “Classification and Regression Trees.“ In

  • Erdal H, Ibrahim, Karakurt O (2013) Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. J Hydrol 477:119–128

    Article  Google Scholar 

  • Fu M, Fan T, Ding Z, Salih SQ, Al-Ansari N, Yaseen ZM (2020) Deep Learning Data-Intelligence Model based on adjusted forecasting window scale: application in Daily Streamflow Simulation. IEEE Access 8:32632–32651

    Article  Google Scholar 

  • Fu Y, Zhuang H, Shen X, Li W (2023) ‘Assessment and prediction of regional climate based on a multimodel ensemble machine learning method’, Clim Dyn : 1–20

  • Ghobadi F, Kang D (2022) Multi-step ahead probabilistic forecasting of Daily Streamflow using bayesian deep learning: a multiple case study. " Water 14(22):3672

    Article  Google Scholar 

  • Haleem K, Khan AU, Ahmad S, Khan M, Khan FA, Khan W, Jehanzeb Khan (2022) Hydrological impacts of climate and land-use change on flow regime variations in upper Indus basin. J Water Clim Change 13:758–770

    Article  Google Scholar 

  • Hancock, Timothy R, Put D, Coomans YV, Heyden, Everingham Y (2005) A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemometr Intell Lab Syst 76:185–196

    Article  Google Scholar 

  • He Q, Wang M, Liu K, Li B, and Ziyu Jiang (2023) Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. Weather Clim Extremes 40:100567

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber Jürgen (1997) Long short-term memory. Neural computation. 9. 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

  • Hu Cai-hong, Wu Q, Li H, Jian Sheng-qi, Li N, and Zhengzheng Lou (2018). ‘Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation’, Water

  • Hu Y, Yan L, Hang T (2020) and Jun Feng. ‘Stream-Flow Forecasting of Small Rivers Based on LSTM’, ArXiv, abs/2001.05681

  • Khan Z, Khan FA, Khan AU, Hussain I, Khan A, Shah LA, Khan J (2022) Yasir Irfan Badrashi, Paweł Kamiński, and Artur Dyczko. 2022. ‘Climate-Streamflow Relationship and Consequences of Its Instability in Large Rivers of Pakistan: An Elasticity Perspective’, Water, 14:2033.

  • Kiran S, Nadia MF, Iqbal (2023) and Irfan Mahmood. ‘Assessing the impacts of climate change on flooding under Coupled Model Intercomparison Project Phase 6 scenarios in the river Chenab, Pakistan’, Natural hazards, 117: 1005-33

  • Kumar V, Kedam N, Sharma KV, Darshan J, Mehta, and Tommaso Caloiero (2023) Advanced Machine Learning Techniques to improve Hydrological Prediction: a comparative analysis of Streamflow Prediction models. Water 15:2572

    Article  Google Scholar 

  • Lu H (2020) and Xin Ma. ‘Hybrid decision tree-based machine learning models for short-term water quality prediction’, Chemosphere, 249: 126169

  • Mahmood R, Jia S, Tripathi NK, Shrestha S (2018) Precipitation Extended Linear Scaling Method for Correcting GCM Precipitation and Its Evaluation and Implication in the Transboundary Jhelum River Basin. Atmosphere 9, 160

  • Nifa K, Boudhar A, Ouatiki H, Elyoussfi H, Bargam B, Chehbouni A (2023) Deep Learning Approach with LSTM for Daily Streamflow Prediction in a semi-arid area: a case study of Oum Er-Rbia River Basin, Morocco. Water 15:262

    Article  Google Scholar 

  • Rahimzad M, Moghaddam Nia A, Zolfonoon H, Soltani J, DANANDEH MEHR, Ali, Kwon H-H (2021) Performance comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning algorithms for Streamflow forecasting. Water Resour Manage 35:1–21. https://doi.org/10.1007/s11269-021-02937-w

    Article  Google Scholar 

  • Rizwan M, Li X, Chen Y, Anjum L, Hamid S, Yamin M, Chauhdary JN Muhammad Adnan Shahid, and Qaisar Mehmood. 2023. ‘Simulating future flood risks under climate change in the source region of the Indus River’, J Flood Risk Manag, 16: e12857

  • Saini V, Kumar R, Kumar, Ameena S, Al-Sumaiti A, Sujil, Ehsan Heydarian-Forushani (2023) Learning based short term wind speed forecasting models for smart grid applications: an extensive review and case study. Electr Power Syst Res 222:109502

    Article  Google Scholar 

  • Singh D, Vardhan M, Sahu R, Chatterjee D, Chauhan P, Shiyin Liu (2023) Machine-learning-and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data. Hydrology Earth System Sciences 27:1047–1075

    Article  Google Scholar 

  • Taye M, Morrow D, Cull J, Smith DH, Hagan M (2023) Deep learning for FAST Quality Assessment. J Ultrasound Med 42:71–79

    Article  Google Scholar 

  • Wu T, Zhang W, Jiao X, Guo W, Yousef Alhaj H (2020) Comparison of five boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables. PLoS ONE 15:e0235324

    Article  Google Scholar 

  • Xu M, Watanachaturaporn P, Varshney PK, Arora MK (2005) ‘Decision tree regression for soft classification of remote sensing data’, Remote Sensing of Environment, 97: 322 – 36

  • Yang Q, Zhang H, Wang G, Luo S, Chen D, Peng W, Junming Shao (2019) Dynamic runoff simulation in a changing environment: a data stream approach. Environ Modelling Softw 112:157–165

    Article  Google Scholar 

  • Yaseen ZM (2021) ‘An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions’, Chemosphere, 277: 130126

  • Zhang X, Qi Y, Liu F, Li H, Shifeng, Sun (2023) Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin. Sci Rep 13:15169

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Civil Engineering, University of Engineering and Technology Peshawar, Bannu Campus, Bannu, 28100, Pakistan

    Basir Ullah, Muhammad Fawad, Afed Ullah Khan, Sikander Khan Mohamand, Mehran Khan & Muhammad Junaid Iqbal

  2. Higher Education Department KPK, Peshawar, Pakistan

    Jehanzeb Khan

  3. National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar, 25000, Pakistan

    Mehran Khan

Authors
  1. Basir Ullah
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  2. Muhammad Fawad
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  3. Afed Ullah Khan
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  4. Sikander Khan Mohamand
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  5. Mehran Khan
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  6. Muhammad Junaid Iqbal
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  7. Jehanzeb Khan
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Contributions

Conceptualization, Writing—original draft, Writing review & editing, Formal analysis, Methodology: Basir Ullah, Afed Ullah Khan, Mehran Khan; Data Curation, Investigation; Muhammad Junaid Iqbal, Muhammad Fawad, Sikhandar and Jehanzeb khan; Supervision: Afed Ullah Khan.

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Correspondence to Afed Ullah Khan.

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Ullah, B., Fawad, M., Khan, A.U. et al. Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models. Water Resour Manage 37, 6089–6106 (2023). https://doi.org/10.1007/s11269-023-03645-3

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  • DOI: https://doi.org/10.1007/s11269-023-03645-3

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