Analyzing public participant data to evaluate citizen satisfaction and to prioritize their needs via K-means, FCM and ICA

Ghodousi, Mostafa; Alesheikh, Ali Asghar; Saeidian, Bahram

doi:10.1016/j.cities.2016.03.015

Cited by 23 publications

(22 citation statements)

References 12 publications

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“…In this equation, T.C.n is the power of the nth empire, f(imperialistn) is the imperialist's fitness, ξ is a positive number between 0 and 1, and mean{f(colonies of empiren)} is the average fitness of the colonies of the empire n. Consideration of small values for ξ causes the power of an entire empire to be nearly equal to the power of its central government (imperialist). By contrast, large values of ξ lead to an increase in the effect of the empire′s colonies on the determination of its total power [66].…”

Section: Modelling Ica Operators In Aw Problemmentioning

confidence: 93%

“…Weaker empires usually fail in the competition, lose their power, and are gradually eliminated. Therefore, during the imperialistic competition, the power of the great empires is gradually increased and the weaker empires are eliminated [65,66]. In the following, the modelling of ICA operators for the AW problem is explained.…”

Section: Modelling Ica Operators In Aw Problemmentioning

confidence: 99%

“…Any empire that cannot increase its strength and lose its power is gradually eliminated during imperialistic competition. In other words, over time, weak empires lose their colonies and stronger empires capture these colonies and increase their power [66]. From the colonies of the weakest empire, N weakest colonies are selected and given to other empires.…”

Section: Modelling Ica Operators In Aw Problemmentioning

confidence: 99%

See 2 more Smart Citations

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Saeidian

Mesgari

Pradhan

et al. 2019

Water

Self Cite

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The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method. and Huang et al. [42] used the GA-PSO hybrid algorithm for the optimal management of water resources. Davijani, Banihabib, Anvar, and Hashemi [9] compared the use of PSO and GA algorithms for allocating water to agricultural, industrial, and municipal consumptions over one year.The ICA is successfully used in a variety of applications related to industrial, civil, mechanical, electrical, and computer engineering [43]. Hosseini-Moghari et al. [44] used this algorithm for water resource management. Karamouz et al. [45] compared the suitability of the ICA and PSO algorithms for allocating reservoir water to agricultural, industrial, and urban consumers. Many studies have used the BA for various applications [46]. However, to the best of our knowledge, it is not used in AW.Considering the preceding review on the use of these algorithms in water resource management and other applications, the reasons for selecting GA, BA, ICA, and PSO algorithms in this study are as follows: (1) They are among the most commonly used meta-heuristic algorithms.(2) On similar problems, the performance of these algorithms was acceptable. (3) The solution space of this study is continuous, while the ICA and PSO are inherently continuous, and GA and BA are also applicable to continuous issues.The aim of this study is to model the AW to farms as...

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Section: Modelling Ica Operators In Aw Problemmentioning

confidence: 93%

Section: Modelling Ica Operators In Aw Problemmentioning

confidence: 99%

Section: Modelling Ica Operators In Aw Problemmentioning

confidence: 99%

See 1 more Smart Citation

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Saeidian

Mesgari

Pradhan

et al. 2019

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…This research used two methods. In the first, such as the study done by Ghodousi et al [23], prioritizing and estimating the satisfaction was done separately, but in the second method, through a new approach, optimizing and estimating the satisfaction was done simultaneously and the result was compared based on CiRM objectives. In this research, BA was used to optimize the clustering.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

et al. 2019

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Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.

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“…Arguably, Social Media Geographic Information (SMGI) analytics integrated with more traditional data sources may potentially improve decision-making and planning processes. Identifying citizens' needs was also the ultimate objective of the study proposed by Ghodousi et al [28] to enhance dialogue between citizens and the government. Data collected through a municipal phone-based request/response system were analyzed to prioritize urban needs using different clustering algorithms and assessed for citizen satisfaction.…”

Section: Introductionmentioning

confidence: 99%

An Analytic Approach to Understanding Process Dynamics in Geodesign Studies

2019

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Recent advances in planning support technologies has enabled interactive collaboration in design processes by multiple stakeholder groups. The available technologies collect and store information on both the evolution of design alternatives and the interactions of participants involved in the design process. However, making sense of available process log-data is still a challenge. This study focuses on process analytics in geodesign studies, where iterative collaboration between stakeholders generates design alternatives and consensus by negotiation. Early findings demonstrate how geodesign process analytics makes it possible to gain insights both in recurrent patterns in participant behavior and in the evolution of the design. The approach, based on the enhanced adaptive structuration theory framework, has been tested using data collected by the Geodesignhub web-based collaborative planning support systems in the Cagliari (Italy) geodesign study.

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Analyzing public participant data to evaluate citizen satisfaction and to prioritize their needs via K-means, FCM and ICA

Cited by 23 publications

References 12 publications

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

An Analytic Approach to Understanding Process Dynamics in Geodesign Studies

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