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

Amodeo, Domenico; Francis, Royce A.

doi:10.1007/s10669-019-09725-3

Environ Syst Decis

2019

DOI: 10.1007/s10669-019-09725-3

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

Domenico Amodeo

Royce A. Francis

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Cited by 4 publications

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“…They compare several techniques including random forests and support vector machines with varying levels of data quality to understand the robustness of the predictive results. Amodeo and Francis (2019) apply classification trees to land parcels based on multiple property-level criteria. The results prioritize parcels which may be most suited for low-impact development projects for storm water management.…”

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confidence: 99%

Advances in machine learning and decision making

Collier¹,

Lambert

Linkov

2019

Environ Syst Decis

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The September issue of Environment Systems & Decisions features several articles utilizing advanced machine learning methodologies to derive insights related to chemical toxicity, building energy sustainability, and storm water management. Along with a review paper on nature-based tourism, and two articles describing case studies in scientific risk communication, this issue highlights both the technical and human aspects of decision making necessary for understanding coupled social, environmental, and infrastructure systems.First, a review article by Mandić (2019) explores the last 10 years of published articles on tourism in protected natural areas. An analysis of the literature uncovers the challenges and opportunities along several dimensions of tourism and ecological management, including governance, conservation, and financing. The findings can be used by those responsible for land management to develop nature-based solutions to mitigate the tensions between ecological, social, and economic priorities.A series of articles in this issue develop and demonstrate machine learning techniques applied to a variety of domains. Varghese et al. (2019) use natural language processing to classify 7000 abstracts on the topic of arsenic, using an active learning approach to classify the abstracts into categories. The authors explore different sampling techniques on the performance of the machine learning algorithms. Adams et al. (2019) investigate the topic of predicting energy consumption in buildings under faulty and uncertain data. They compare several techniques including random forests and support vector machines with varying levels of data quality to understand the robustness of the predictive results. Amodeo and Francis (2019) apply classification trees to land parcels based on multiple property-level criteria. The results prioritize parcels which may be most suited for low-impact development projects for storm water management.Finally, in a set of two companion papers, Quigley et al. (2019a, b) discuss the role of scientific information on riskbased decision making. In the first paper (Quigley et al. 2019a), seven case studies are described, including farming, mining, and natural disaster management. Across these case studies, the authors detail how the various contents and delivery mechanisms of scientific information influenced decision making. The authors describe the different sources of scientific information, and how the absence of scientific information can result in more precautionary alternatives being selected. In the second paper (Quigley et al. 2019b), a synthesis of the findings from the case studies identifies multiple actions and pathways for the acquisition of scientific information and provision of information to the audience. Together, these companion papers will be of value to the scientific and policy communities engaged in using scientific information for decision making and communicating the scientific information to decision makers and the public.Recently, Google Scholar published their year...

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confidence: 99%

Advances in machine learning and decision making

Collier¹,

Lambert

Linkov

2019

Environ Syst Decis

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Evaluation of stormwater management approaches and challenges in urban flood control

Arya

Kumar

2023

Urban Climate

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Urban household uptake of water sensitive urban design source control measures: an exploratory comparative survey across Cape Town and Pretoria, South Africa

Toit

Chilwane

2021

Urban Water Journal

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

Cited by 4 publications

References 15 publications

Advances in machine learning and decision making

Advances in machine learning and decision making

Evaluation of stormwater management approaches and challenges in urban flood control

Urban household uptake of water sensitive urban design source control measures: an exploratory comparative survey across Cape Town and Pretoria, South Africa

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