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Modeling the Relationship between Catchment Attributes and In-stream Water Quality

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  • Iman Fatehi
  • Bahman Amiri
  • Afshin Alizadeh
  • Jan Adamowski

Abstract

The physical attributes of catchments have a significant influence on the chemistry and physical features of in-stream water quality. Consequently, modeling this relationship is important for informing management strategies aimed at improving regional water quality. This study used a machine learning approach (Artificial Neural Networks or ANNs) to model the relationship between land use/cover, associated with other physical attributes of the catchment such as geological permeability and hydrologic soil groups, and in-stream water quality parameters (e.g., K + , Na + , Mg 2+ , Ca 2+ , SO 4 2− , Cl − , HCO 3 − , SAR, pH, EC, TDS). Eighty-eight catchments in the southern basins of the Caspian Sea were explored. To enhance the architecture of ANNs, the study applied backward elimination-based multiple linear regression, through which the optimum input nodes of ANNs can be determined amongst the most relevant variables. A transformation approach was also applied to qualify the performance of ANNs in four quality classes, ranging from unsatisfactory to very good. According to the findings, ANN based TDS model performance improved from unsatisfactory to very good. However, the linear regression-based pH model resulted in a decrease in performance, from “very good” to satisfactory. Moreover, among all catchment attributes, urban areas had the greatest impact on K + , Na + , Mg 2+ , Cl − , SO 4 2− , EC and SAR concentration values. K + , TDS and EC were influenced by agricultural area (%). Bare land areas (%) had the largest impact on Na + , Ca 2+ and HCO 3 − . Assessing the performance of the ANN-based models developed in this study indicates that 10 out of 11 models had “very good” quality ratings and can be reliably used in practice. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Iman Fatehi & Bahman Amiri & Afshin Alizadeh & Jan Adamowski, 2015. "Modeling the Relationship between Catchment Attributes and In-stream Water Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5055-5072, November.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:14:p:5055-5072
    DOI: 10.1007/s11269-015-1103-y
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    References listed on IDEAS

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    1. Bahman Jabbarian Amiri & Gao Junfeng & Nicola Fohrer & Felix Mueller & Jan Adamowski, 2018. "Regionalizing Flood Magnitudes using Landscape Structural Patterns of Catchments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2385-2403, May.
    2. Mirzaei, Mohsen & Jafari, Ali & Gholamalifard, Mehdi & Azadi, Hossein & Shooshtari, Sharif Joorabian & Moghaddam, Saghi Movahhed & Gebrehiwot, Kindeya & Witlox, Frank, 2020. "Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover," Land Use Policy, Elsevier, vol. 95(C).
    3. Maksym Łaszewski & Michał Fedorczyk & Sylwia Gołaszewska & Zuzanna Kieliszek & Paulina Maciejewska & Jakub Miksa & Wiktoria Zacharkiewicz, 2021. "Land Cover Effects on Selected Nutrient Compounds in Small Lowland Agricultural Catchments," Land, MDPI, vol. 10(2), pages 1-20, February.
    4. Mehdi Vafakhah & Saeid Khosrobeigi Bozchaloei, 2020. "Regional Analysis of Flow Duration Curves through Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 283-294, January.

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