A new multiple regression model for predictions of urban water use

Eslamian, S,; Li, S. Samuel; Haghighat, Fariborz

doi:10.1016/j.scs.2016.08.003

Cited by 34 publications

(9 citation statements)

References 16 publications

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“…These authors believe that intelligent computing will enable the various elements of a city to operate efficiently and intelligently. The literature citing this paper found that its followers understand the driving forces of smart cities [99,114,128], the opportunities and challenges surrounding smart mobile devices [76,129], and the uses and development of smart water networks [130,131]. The burst strength of the paper by Cardone et al [110] is 4.752, and this research was widely quoted from 2014 to 2016.…”

Section: Reference Burst-detection Analysismentioning

confidence: 96%

Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and Scientometric Analysis

2019

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As urbanization continues to accelerate, the number of cities and their growing populations have created problems, such as the congestion and noise related to transportation, the pollution from industry, and the difficulty of disposing of garbage. An emerging urban strategy is to make use of digital technologies and big data to help improve the quality of life of urban residents. In the past decade, more and more researchers have studied smart cities, and the number of literature in this field grows rapidly, making it “big data”. With the aim of better understanding the contexts of smart-city research, including the distribution of topics, knowledge bases, and the research frontiers in the field, this paper is based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) in the Web of Science (WoS) Core Collection, and the method used is that of comprehensive scientometric analysis and knowledge mapping in terms of diversity, time slicing, and dynamics, using VOSviewer and CiteSpace to study the literature in the field. The main research topics can be divided into three areas—“the concepts and elements of the smart city”, “the smart city and the Internet of Things”, and “the smart city of the future”—through document co-citation analysis. There are four key directions—“research objectives and development-strategy research”, “technical-support research”, “data-processing and applied research”, and “management and applied research”—analyzed using keywords co-occurrence. Finally, the research frontiers are urban-development, sustainable cities, cloud computing, artificial intelligence, integration, undertaken through keyword co-occurrence analysis.

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Section: Reference Burst-detection Analysismentioning

confidence: 96%

Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and Scientometric Analysis

2019

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“…In a simple linear regression (SLR) model, there is only one explanatory variable, whereas in a multiple linear regression model, multiple explanatory variables are utilised to predict the outcome of a response variable assuming the relationship between the explanatory variables and the response variable as linear. Multiple linear regression is basically the extension of simple linear or ordinary least squares (OLS) regression by allowing more than one explanatory variable to rely on the mean function E(Y) [16]. A usual representation of multiple linear regression is: where Y and X i (i = 1, 2, …, k) represent response and explanatory variables, respectively.…”

Section: Simple and Multiple Linear Regression Modelmentioning

confidence: 99%

Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

2021

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Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/m 2 ) at 30 DAS and total weed density (no./m 2 ) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables.

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“…Most researchers focus on residential water demand forecasting in order to manage the currently available water sources in use and plan for future needs. They classify water demand into two major categories: base water demand and seasonal water demand [5].…”

Section: Introductionmentioning

confidence: 99%

The Assessment of Climate Variables and Geographical Distribution on Residential Drinking Water Demand in Ethiopia

Timotewos

Barjenbruch

Behailu

2022

Water

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Water managers have increasingly shown that demand management solutions are more important than searching for alternative sources to resolve the challenges and shortages of water supply services. This study identifies the impact of climate variables on residential water demand in three geographically and spatially dispersed towns (Arba Minch, Ziway, and Debre Birhan) in Ethiopia. Monthly mean temperature, relative humidity, and precipitation are analyzed using multivariate regression models to identify and evaluate the impacts of the parameters on water consumption. Principal component analysis (PCA) is also used to determine the dominant independent variable affecting the rate of water consumption. Mean temperature is shown to be the dominant variable causing the changes in water consumption in Arba Minch. The water consumption at Debre Birhan is slightly affected by relative humidity. Analyzed climate variables do not affect the water consumption changes at Ziway. The main findings of this paper show that geographical distribution and other determinants are more important determinants of residential water demand. It is concluded that the analyzed climate variables are not the dominant determinants which impact drinking water consumption at the study sites. Thus, it is recommended to include relevant information about the climate variables alongside other determinants in order to enhance the water management system in evaluating and auditing water usage.

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A new multiple regression model for predictions of urban water use

Cited by 34 publications

References 16 publications

Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and Scientometric Analysis

Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and Scientometric Analysis

Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models

The Assessment of Climate Variables and Geographical Distribution on Residential Drinking Water Demand in Ethiopia

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