Optimum Allocation of Water to the Cultivation Farms Using Genetic Algorithm

Saeidian, Bahram; Mesgari, Mehran; Ghodousi, Mostafa

doi:10.5194/isprsarchives-xl-1-w5-631-2015

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Selection of proper measures for the evaluation of clustering algorithms depends on the available information and utilized methods [42,43]. Two types of evaluation measures have been used in the literature: internal indices and external indices.…”

Section: Quality Measuresmentioning

confidence: 99%

A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data

Ghaemi

Farnaghi

2019

IJGI

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Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged tweets provides the ability to discover events and their locations. DBSCAN (density-based spatial clustering of applications with noise), which has been widely used to retrieve events from geotagged tweets, cannot efficiently detect clusters when there is significant spatial heterogeneity in the dataset, as it is the case for Twitter data where the distribution of users, as well as the intensity of publishing tweets, varies over the study areas. This study proposes VDCT (Varied Density-based spatial Clustering for Twitter data) algorithm that extracts clusters from geotagged tweets by considering spatial heterogeneity. The algorithm employs exponential spline interpolation to determine different search radiuses for cluster detection. Moreover, in addition to spatial proximity, textual similarities among tweets are also taken into account by the algorithm. In order to examine the efficiency of the algorithm, geotagged tweets collected during a hurricane in the United States were used for event detection. The output clusters of VDCT have been compared to those of DBSCAN. Visual and quantitative comparison of the results proved the feasibility of the proposed method.

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Section: Quality Measuresmentioning

confidence: 99%

A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data

Ghaemi

Farnaghi

2019

IJGI

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“…They have the ability to solve complex optimization problems in wide and many-dimensional search spaces. Artificial intelligence techniques are widely used and considered in many computing research [14,15]. In a paper by Yi and Kumar [16], provision of the facilities is divided into two phases of determining the vehicle routes and dispatching the relief commodity, which are optimized using the ACO algorithm.…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

Saeidian

Mesgari

Pradhan

et al. 2018

IJGI

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After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.

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“…Among these methods are meta-heuristic algorithms. Meta-heuristic algorithms have been used in different engineering and management issues [15]. Also, studies in CRM showed that meta-heuristic algorithms increase clustering quality.…”

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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Optimum Allocation of Water to the Cultivation Farms Using Genetic Algorithm

Cited by 7 publications

References 17 publications

A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data

A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data

Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

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

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