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

Water Resources Management volume 36, pages 4255–4273 (2022)Cite this article

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.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Watershed Management Engineering, Tarbiat Modares University, Tehran, Iran

    Vahid Moosavi

  2. Pooyeshgaran Forogh Fardad Consulting Eng., Science and Technology Park, Yazd, Iran

    Ayoob Karami

  3. Department of Arid Lands Management, Yazd University, Yazd, Iran

    Negin Behnia

  4. Centre for Advanced Middle Eastern Studies & Division of Water Resources Engineering, Lund University, Lund, Sweden

    Ronny Berndtsson

  5. Research Institute for Geo-Hydrological Protection, Via Madonna Alta 126, 06128, Perugia, Italy

    Christian Massari

Authors
  1. Vahid Moosavi
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  2. Ayoob Karami
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  3. Negin Behnia
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  4. Ronny Berndtsson
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  5. Christian Massari
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Contributions

V. Moosavi: Conceptualization; Formal analysis; Investigation; Project administration; Supervision; Software; Roles/Writing—original draft. A. Karami: Conceptualization; Data curation; Formal analysis; Validation; Visualization; Software; Writing—review & editing. N. Behnia: Conceptualization; Data curation; Formal analysis; Software; Methodology; Visualization. R. Berndtsson and Ch. Massari: Conceptualization; Writing—review & editing; Methodology.

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Correspondence to Vahid Moosavi.

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Moosavi, V., Karami, A., Behnia, N. et al. Linking Hydro-Physical Variables and Landscape Metrics using Advanced Data Mining for Stream-Flow Prediction. Water Resour Manage 36, 4255–4273 (2022). https://doi.org/10.1007/s11269-022-03251-9

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  • DOI: https://doi.org/10.1007/s11269-022-03251-9

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