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Linking Hydro-Physical Variables and Landscape Metrics using Advanced Data Mining for Stream-Flow Prediction

Author

Listed:
  • Vahid Moosavi

    (Tarbiat Modares University)

  • Ayoob Karami

    (Pooyeshgaran Forogh Fardad Consulting Eng., Science and Technology Park)

  • Negin Behnia

    (Yazd University)

  • Ronny Berndtsson

    (Lund University)

  • Christian Massari

    (Research Institute for Geo-Hydrological Protection)

Abstract

In Streamflow prediction the most important triggering/controlling variables are related to climate, physiography, and landscape patterns. This study investigated the effect of different landscape metrics to relate spatial patterns to surface runoff processes and predict monthly streamflow using climatic and physiographic variables for the 42 sub-basins of the Urmia Lake Basin in Iran. We developed an innovative data-driven framework and considered two different modelling approaches i.e., modelling in homogenous clusters (local approach) and modelling in the entire area as an entity (global approach). The results of basin LULC monitoring from the 20-year experimental period display drastic changes in the land use of the basin such as reduction in lake area (48.3%) due to increasing irrigated areas (22.5%), increasing residential areas (14.2%), and decrease in rangeland (6.0%). Streamflow prediction results in the global experiment showed Group Method of Data Handling (GMDH) and Random Forest (RF) with NSE of 0.76 and NRMSE of 6.44% have similar results and outperformed Partial Least Squares regression (PLS), but in clustering experiment GMDH with NSE of 0.88 and NRMSE of 5% shows the highest accuracy and outperformed both RF and PLS. The results confirmed that modelling in homogenous clusters (local prediction) significantly enhanced the performance of prediction.

Suggested Citation

  • Vahid Moosavi & Ayoob Karami & Negin Behnia & Ronny Berndtsson & Christian Massari, 2022. "Linking Hydro-Physical Variables and Landscape Metrics using Advanced Data Mining for Stream-Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4255-4273, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:11:d:10.1007_s11269-022-03251-9
    DOI: 10.1007/s11269-022-03251-9
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    References listed on IDEAS

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    1. Vahid Moosavi & Ali Talebi & Mohammad Reza Hadian, 2017. "Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 43-59, January.
    2. Boongaling, Cheamson Garret K. & Faustino-Eslava, Decibel V. & Lansigan, Felino P., 2018. "Modeling land use change impacts on hydrology and the use of landscape metrics as tools for watershed management: The case of an ungauged catchment in the Philippines," Land Use Policy, Elsevier, vol. 72(C), pages 116-128.
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    Cited by:

    1. Yinshan Xu & Yubin Chen & Yufeng Ren & Zhengyang Tang & Xu Yang & Yu Zhang, 2023. "Attribution of Streamflow Changes Considering Spatial Contributions and Driver Interactions Based on Hydrological Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 1859-1877, March.

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