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Investigating adoption patterns of residential low impact development (LID) using classification trees

Author

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  • Domenico C. Amodeo

    (The George Washington University)

  • Royce A. Francis

    (The George Washington University)

Abstract

Local governments are under pressure to improve storm water management and often times must comply with consent decrees with the Federal Government. Decentralizing a portion of the storm water management by integrating private landowners into localized retention and infiltration efforts, that is, low impact development (LID) or green infrastructure projects, is becoming increasingly popular. Some wastewater systems have considered incentivizing private land owners to make improvements aimed at retaining storm water or slowing the conveyance to grey infrastructure. This study examines potential opportunities for incentivizing private residential land owners in Washington DC to install LID projects. This study maps LID configurations to a set of adoption strategies and categories. The C4.5 algorithm is then applied to identify a high performance decision tree for classifying parcels by adoption strategy or adoption categories based on property-level attributes.

Suggested Citation

  • Domenico C. Amodeo & Royce A. Francis, 2019. "Investigating adoption patterns of residential low impact development (LID) using classification trees," Environment Systems and Decisions, Springer, vol. 39(3), pages 295-306, September.
    • Handle: RePEc:spr:envsyd:v:39:y:2019:i:3:d:10.1007_s10669-019-09725-3
    DOI: 10.1007/s10669-019-09725-3
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    References listed on IDEAS

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    Cited by:

    1. Zachary A. Collier & James H. Lambert & Igor Linkov, 2019. "Advances in machine learning and decision making," Environment Systems and Decisions, Springer, vol. 39(3), pages 247-248, September.

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