The relevance of aggregating a water consumption model cannot be disconnected from the choice of information available on the resource

Edwards, Margaret; Ferrand, Nils; Goreaud, François; Huet, Sylvie

doi:10.1016/j.simpat.2004.11.008

Cited by 31 publications

(15 citation statements)

References 21 publications

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“…Monotonous interpolation or constant imputation will be used according to the knowledge on the variables provided by the expert. For example, in water resources management, different levels of aggregation regarding a water consumption model cannot be disconnected between them and from the global information available on the resources [60] [105]. This coexistence of different granularities in both time and space, in geospatial phenomena, characteristic in ESs, is a reason why learning global models for environmental phenomena is so difficult [16].…”

Section: Treatmentmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.

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Section: Treatmentmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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“…On the household scale, spatial effects may include interhousehold relationships. For example, the change in water use behaviors and the adoption of new technologies and methods do not only depend on personal interest but also are influenced by social pressures, especially from neighboring households (Edwards et al, 2005). Wentz and Gober (2007) also find spatial effects on the census tract scaleneighboring census tracts exhibit similar water use behaviors.…”

Section: Models With Common Variablesmentioning

confidence: 99%

A Multi‐Scale Analysis of Single‐Family Residential Water Use in the Phoenix Metropolitan Area

Wentz

Ruddell

et al. 2013

J American Water Resour Assoc

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Studies that evaluate determinants of residential water demand typically use data from a single spatial scale. Although household‐scale data are preferred, especially when econometric models are used, researchers may be limited to aggregate data. There is little, if any, empirical analysis to assess whether spatial scale may lead to ecological fallacy problems in residential water use research. Using linear mixed‐effects models, we compare the results for the relationship of single‐family water use with its determinants using data from the household and census tract scales in the city of Phoenix. Model results between the household and census tract scale are similar suggesting the ecological fallacy may not be significant. Common significant determinants on these two spatial scales include household size, household income, house age, pool size, irrigable lot size, precipitation, and temperature. We also use city/town scale data from the Phoenix metropolitan area to parameterize the linear mixed‐effects model. The difference in the parameter estimates of those common variables compared to the first two scales indicates there is spatial heterogeneity in the relationship between single‐family water use and its determinants among cities and towns. The negative relationship between single‐family house density and residential water use suggests that residential water consumption could be reduced through coordination of land use planning and water demand management.

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“…4 to calculate the agent's utility to cooperate using Eqs. 2 through 6 (adapted and modified from Edwards et al (2005) adaptation of Young (1999) sociologic diffusion model for residential water domains). These equations' parameters are presented in Table 1.…”

Section: Behavioral Simulation Modelmentioning

confidence: 99%

Managing Water Resources Conflicts: Modelling Behavior in a Decision Tool

Akhbari

Grigg

2015

Water Resour Manage

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While technical tools for analysis of water resources systems have advanced, the major issue in resolving problems focuses on the interaction of human and natural systems. Agent-based modeling (ABM) has recently been used as an effective tool to develop integrated human-environmental models. One of the main challenges of ABM application in water resources management is to identify and characterize key agents. We provided recommendations to characterize agents normally involved in water decisions, and developed a framework for a conflict management tool, comprised of three models: a watershed simulation, an optimization, and a behavioral simulation model. The optimization-simulation model determined tradeoffs between the objective functions. The behavioral simulation model, developed based on ABM, simulated stakeholders interactions and their reactions to water allocation decisions. This model evaluated the applicability of different management scenarios to achieve specific rates of reduction in agricultural water allocations, selected from the tradeoffs. To develop and adjust this model, key stakeholders were identified in the San Joaquin River (SJR) watershed, California, and a survey was administered. The proposed recommendations and framework provides a new and innovative way to identify institutional interconnections, formulate and simulate their interactions, and create a hydrologic-environmental-human interface to support powerful decision-support tools to manage conflicts and make informed, practical decisions in water resources.

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The relevance of aggregating a water consumption model cannot be disconnected from the choice of information available on the resource

Cited by 31 publications

References 21 publications

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

A Multi‐Scale Analysis of Single‐Family Residential Water Use in the Phoenix Metropolitan Area

Managing Water Resources Conflicts: Modelling Behavior in a Decision Tool

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