Data Mining Practical Machine Learning Tools and Techniques

As the term “smart city” gains wider and wider currency, there is still confusion about what a smart city is, especially since several similar terms are often used interchangeably. This paper aims to clarify the meaning of the word “smart” in the context of cities through an approach based on an in-depth literature review of relevant studies as well as official documents of international institutions. It also identifies the main dimensions and elements characterizing a smart city. The different metrics of urban smartness are reviewed to show the need for a shared definition of what constitutes a smart city, what are its features, and how it performs in comparison to traditional cities. Furthermore, performance measures and initiatives in a few smart cities are identified.

Smart Cities: Definitions, Dimensions, Performance, and Initiatives

Making a city "smart" is emerging as a strategy to mitigate the problems generated by the urban population growth and rapid urbanization. Yet little academic research has sparingly discussed the phenomenon. To close the gap in the literature about smart cities and in response to the increasing use of the concept, this paper proposes a framework to understand the concept of smart cities. Based on the exploration of a wide and extensive array of literature from various disciplinary areas we identify eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. These factors form the basis of an integrative framework that can be used to examine how local governments are envisioning smart city initiatives. The framework suggests directions and agendas for smart city research and outlines practical implications for government professionals.

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Understanding Smart Cities: An Integrative Framework

This conceptual paper discusses how we can consider a particular city as a smart one, drawing on recent practices to make cities smart. A set of the common multidimensional components underlying the smart city concept and the core factors for a successful smart city initiative is identified by exploring current working definitions of smart city and a diversity of various conceptual relatives similar to smart city. The paper offers strategic principles aligning to the three main dimensions (technology, people, and institutions) of smart city: integration of infrastructures and technology-mediated services, social learning for strengthening human infrastructure, and governance for institutional improvement and citizen engagement.

https://inta-aivn.org/images/cc/Urbanism/background%20documents/dgo_2011_smartcity.pdf

Conceptualizing smart city with dimensions of technology, people, and institutions

This paper sees a smart city not as a status of how smart a city is but as a city's effort to make itself smart. The connotation of a smart city represents city innovation in management and policy as well as technology. Since the unique context of each city shapes the technological, organizational and policy aspects of that city, a smart city can be considered a contextualized interplay among technological innovation, managerial and organizational innovation, and policy innovation. However, only little research discusses innovation in management and policy while the literature of technology innovation is abundant. This paper aims to fill the research gap by building a comprehensive framework to view the smart city movement as innovation comprised of technology, management and policy. We also discuss inevitable risks from innovation, strategies to innovate while avoiding risks, and contexts underlying innovation and risks.

Smart city as urban innovation: focusing on management, policy, and context

Statistical learning theory is the basis of support vector machine (SVM) technique. This technique in natural hazard assessment is getting extremely popular these days. It contains a training stage related to the input and desire output values. The main goal of this paper is to assess and evaluate the prediction capability of SVM technique with different kernel functions for spatial prediction of flood occurrence. Kuala Terengganu basin, Malaysia was selected as study area. To begin, a flood inventory map was produced by mapping the flood locations in the Terengganu using documentary sources and field survey. The flood inventory was partitioned into training and testing datasets through random selection. The spatial database was constructed using various flood conditioning factors: altitude, slope, curvature, stream power index (SPI), topographic wetness index (TWI), distance from the river, geology, land use/cover (LULC), soil, and surface runoff. Four SVM kernel types such as linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG) were utilized to check the robustness of the SVM model. Consequently, four flood susceptibility maps were created. In order to examine the efficiency of the SVM model, a probabilistic based frequency ratio (FR) model was applied and compared with the SVM outcomes. An area under the curve (AUC) method was used to validate the resultant flood susceptibility maps. The validation results demonstrated that the prediction rate curves for flood susceptibility maps generated by the SVM-LN, SVM-PL, SVM-RBF, and SVM-SIG were 84.63%, 83.92%, 84.97%, and 81.88% respectively. On the other hand, the prediction rate achieved by FR showed the lowest accuracy of 61.43%. To evaluate the impacts of conditioning factors on the flood susceptibility mapping, Cohen's kappa index was measured. The result demonstrated that all conditioning factors have reasonably positive influence on the flood analysis in current case study except surface runoff which decreased the accuracy of the final results. The most influential factors were altitude and slope for all kernel types. It can be concluded that SVM technique is an efficient and reliable tool in flood susceptibility assessment. The resultant flood susceptibility maps can be beneficial in flood mitigation strategies.

Flood susceptibility assessment using GIS-based support vector machine model with different kernel types

In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate...

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Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Agriculture Land Suitability Evaluator (ALSE)

The research is essentially to modularize the structure of utilities and develop a system for following up the activities electronically on the city scale. The GIS operational platform will be the base for managing the infrastructure development components with the systems interoperability for the available city infrastructure related systems. The concentration will be on the available utility networks in order to develop a comprehensive, common, standardized geospatial data models. The construction operations for the utility networks such as electricity, water, Gas, district cooling, irrigation, sewerage and communication networks; are need to be fully monitored on daily basis, in order to utilize the involved huge resources and man power. These resources are allocated only to convey the operational status for the construction and execution sections that used to do the required maintenance. The need for a system that serving the decision makers for following up these activities with a proper geographical representation will definitely reduce the operational cost for the long term.

Smart City Components Architicture

https://cyberleninka.org/article/n/1024060.pdf

Noordin Ahmad

Papers

Flood susceptibility assessment using GIS-based support vector machine model with different kernel types

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Agriculture Land Suitability Evaluator (ALSE)

Smart City Components Architicture

Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran