IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i10p4005-d1392028.html
   My bibliography  Save this article

A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting

Author

Listed:
  • Antoifi Abdoulhalik

    (Department of Civil and Environmental Engineering, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK)

  • Ashraf A. Ahmed

    (Department of Civil and Environmental Engineering, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK)

Abstract

This study examines the contribution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin. Different sets of scenarios included rainfall data from different weather stations located in various geographical locations with respect to the flow monitoring station. Long short-term memory (LSTM)-based models were used to examine the contribution of rainfall data on streamflow-forecasting performance by investigating five scenarios whereby RF data from different weather stations were incorporated depending on their geographical positions. Specifically, the All-RF scenario included all rainfall data collected at 11 stations; Upstream-RF (Up-RF) and Downstream-RF (Down-RF) included only the rainfall data measured upstream and downstream of the streamflow-measuring station; Pearson-RF (P-RF) only included the rainfall data exhibiting the highest level of correlation with the streamflow data, and the Flow-only (FO) scenario included streamflow data. The evaluation metrics used to quantitively assess the performance of the models included the RMSE, MAE, and the coefficient of determination, R 2 . Both ML models performed best in the FO scenario, which shows that the diversity of input features (hydrological and meteorological data) did not improve the predictive accuracy regardless of the positions of the weather stations. The results show that the P-RF scenarios yielded better prediction accuracy compared to all the other scenarios including rainfall data, which suggests that only rainfall data upstream of the flow monitoring station tend to make a positive contribution to the model’s forecasting performance. The findings evidence the suitability of simple monolayer LSTM-based networks with only streamflow data as input features for high-performance and budget-wise river flow forecast applications while minimizing data processing time.

Suggested Citation

  • Antoifi Abdoulhalik & Ashraf A. Ahmed, 2024. "A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting," Sustainability, MDPI, vol. 16(10), pages 1-15, May.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4005-:d:1392028
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/10/4005/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/10/4005/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tobias Siegfried & Thomas Bernauer & Renaud Guiennet & Scott Sellars & Andrew Robertson & Justin Mankin & Peter Bauer-Gottwein & Andrey Yakovlev, 2012. "Will climate change exacerbate water stress in Central Asia?," Climatic Change, Springer, vol. 112(3), pages 881-899, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. R. Bryson Touchstone & Kathleen Sherman-Morris, 2016. "Vulnerability to prolonged cold: a case study of the Zeravshan Valley of Tajikistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(2), pages 1279-1300, September.
    2. Li, Zhi & Fang, Gonghuan & Chen, Yaning & Duan, Weili & Mukanov, Yerbolat, 2020. "Agricultural water demands in Central Asia under 1.5 °C and 2.0 °C global warming," Agricultural Water Management, Elsevier, vol. 231(C).
    3. Bhaduri, Anik & Djanibekov, Nodir, 2015. "Adoption of Water-Efficient Technology: Role of Water Price Flexibility, Tenure Uncerntainty and Production Targets in Uzbekistan," 2015 Conference, August 9-14, 2015, Milan, Italy 211336, International Association of Agricultural Economists.
    4. Bobojonov, Ihtiyor & Aw-Hassan, Aden, 2014. "Impacts of climate change on farm income security in Central Asia: An integrated modeling approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 188, pages 245-255.
    5. Huili He & Rafiq Hamdi & Geping Luo & Peng Cai & Xiuliang Yuan & Miao Zhang & Piet Termonia & Philippe Maeyer & Alishir Kurban, 2022. "The summer cooling effect under the projected restoration of Aral Sea in Central Asia," Climatic Change, Springer, vol. 174(1), pages 1-21, September.
    6. Shan Zou & Abuduwaili Jilili & Weili Duan & Philippe De Maeyer & Tim Van de Voorde, 2019. "Human and Natural Impacts on the Water Resources in the Syr Darya River Basin, Central Asia," Sustainability, MDPI, vol. 11(11), pages 1-18, May.
    7. Bin Guo & Zhongsheng Chen & Jinyun Guo & Feng Liu & Chuanfa Chen & Kangli Liu, 2016. "Analysis of the Nonlinear Trends and Non-Stationary Oscillations of Regional Precipitation in Xinjiang, Northwestern China, Using Ensemble Empirical Mode Decomposition," IJERPH, MDPI, vol. 13(3), pages 1-20, March.
    8. Wen Liu & Long Ma & Yaoming Li & Jilili Abuduwaili & Salamat Abdyzhapar uulu, 2020. "Heavy Metals and Related Human Health Risk Assessment for River Waters in the Issyk−Kul Basin, Kyrgyzstan, Central Asia," IJERPH, MDPI, vol. 17(10), pages 1-13, May.
    9. Oimahmad Rahmonov & Bartłomiej Szypuła & Michał Sobala & Zebiniso B. Islamova, 2024. "Environmental and Land-Use Changes as a Consequence of Land Reform in the Urej River Catchment (Western Tajikistan)," Resources, MDPI, vol. 13(4), pages 1-22, April.
    10. Feng Chen & Yujiang Yuan & Nicole Davi & Tongwen Zhang, 2016. "Upper Irtysh River flow since AD 1500 as reconstructed by tree rings, reveals the hydroclimatic signal of inner Asia," Climatic Change, Springer, vol. 139(3), pages 651-665, December.
    11. Uchechukwu Jarrett & Yvonne Tackie, 2024. "Re‐examining the effect of heat and water stress on agricultural output growth: How is Sub‐Saharan Africa different?," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 515-530, May.
    12. Chaofan Li & Qifei Han & Geping Luo & Chengyi Zhao & Shoubo Li & Yuangang Wang & Dongsheng Yu, 2018. "Effects of Cropland Conversion and Climate Change on Agrosystem Carbon Balance of China’s Dryland: A Typical Watershed Study," Sustainability, MDPI, vol. 10(12), pages 1-16, November.
    13. Khasanov, Sayidjakhon & Li, Fadong & Kulmatov, Rashid & Zhang, Qiuying & Qiao, Yunfeng & Odilov, Sarvar & Yu, Peng & Leng, Peifang & Hirwa, Hubert & Tian, Chao & Yang, Guang & Liu, Hongguang & Akhmato, 2022. "Evaluation of the perennial spatio-temporal changes in the groundwater level and mineralization, and soil salinity in irrigated lands of arid zone: as an example of Syrdarya Province, Uzbekistan," Agricultural Water Management, Elsevier, vol. 263(C).
    14. Christopher White & Trevor Tanton & David Rycroft, 2014. "The Impact of Climate Change on the Water Resources of the Amu Darya Basin in Central Asia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5267-5281, December.
    15. Wanlu Liu & Lulu Liu & Jiangbo Gao, 2020. "Adapting to climate change: gaps and strategies for Central Asia," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(8), pages 1439-1459, December.
    16. Tobias Siegfried & Aziz Ul Haq Mujahid & Beatrice Marti & Peter Molnar & Dirk Nikolaus Karger & Andrey Yakovlev, 2024. "Unveiling the future water pulse of central asia: a comprehensive 21st century hydrological forecast from stochastic water balance modeling," Climatic Change, Springer, vol. 177(9), pages 1-19, September.
    17. Bekchanov, Maksud, 2024. "Conveyance efficiency and irrigation water productivity under varying water supply conditions in arid lowlands of Central Asia," Agricultural Water Management, Elsevier, vol. 293(C).
    18. Marzhan Baigaliyeva & Zauresh Atakhanova & Akbota Kairat, 2024. "Spatiotemporal Variations in Urban Wetlands in Kazakhstan: A Case of the Taldykol Lake System in Astana City," Sustainability, MDPI, vol. 16(16), pages 1-26, August.
    19. Djanibekov, Nodir & Djanibekov, Utkur & Sommer, Rolf & Petrick, Martin, 2015. "Cooperative agricultural production to exploit individual heterogeneity under a delivery target: The case of cotton in Uzbekistan," Agricultural Systems, Elsevier, vol. 141(C), pages 1-13.
    20. Iulii Didovets & Valentina Krysanova & Aliya Nurbatsina & Bijan Fallah & Viktoriya Krylova & Assel Saparova & Jafar Niyazov & Olga Kalashnikova & Fred Fokko Hattermann, 2024. "Attribution of current trends in streamflow to climate change for 12 Central Asian catchments," Climatic Change, Springer, vol. 177(1), pages 1-20, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4005-:d:1392028. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.