Optimising sampling designs for the maximum coverage problem of plume detection

Helle, Kristina Barbara; Pebesma, Edzer

doi:10.1016/j.spasta.2015.03.004

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…Equation (10) demonstrates that, for a particular distribution, Ω (l→m) α is a function only of the parameter α and frequencies of occurrences of the phenomena n l and n m in the original distribution, between which the exchange of the element occurs. Equation ( 10) further shows that if the exchange of the element occurs between phenomena l and m of the same (similar) frequencies of occurrence (i.e., n l ≈ n m ), the value of Ω = 0.…”

Section: Point Divergence Gainmentioning

confidence: 99%

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Point Divergence Gain and Multidimensional Data Sequences Analysis

Rychtáriková

Korbel

Macháček

et al. 2018

Entropy

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Abstract:We introduce novel information-entropic variables-a Point Divergence Gain (Ω (l→m) α ), a Point Divergence Gain Entropy (I α ), and a Point Divergence Gain Entropy Density (P α )-which are derived from the Rényi entropy and describe spatio-temporal changes between two consecutive discrete multidimensional distributions. The behavior of Ω (l→m) α is simulated for typical distributions and, together with I α and P α , applied in analysis and characterization of series of multidimensional datasets of computer-based and real images.

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Section: Point Divergence Gainmentioning

confidence: 99%

“…For the overview of model-based approaches in the random field theory, one can consult, e.g., Refs. [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Point Divergence Gain and Multidimensional Data Sequences Analysis

Rychtáriková

Korbel

Macháček

et al. 2018

Entropy

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“…In this case, the problem with the location and status of each ambulance and all emergency contacts received up to time t were defined [18]. By explaining the prioritization of location indicators, the proposed algorithm optimizes the mathematical model and determines the number and location of ambulances with the assumptions made [19].…”

Section: Introductionmentioning

confidence: 99%

Presenting A Multi-objective Location Model in GIS Environment: A Case Study in East Tehran Emergency Stations

Mahmoudi

Pishvaei

Saberian

et al. 2021

HDQ

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Background: This study aims to design a multi-objective model for locating emergency relief bases with maximum coverage and minimum costs. As a result, this model is expected to reduce mortality by increasing the efficiency of relief services. Materials and Methods: Based on the ambulance distribution in the east of Tehran City, Iran, and the statistical information on demand (at least 30 samples for data normalization) in the last six months of 2018, we introduced and implemented a definitive mathematical model. We also evaluated the model with GAMS software. Using previous studies and interviews, we identified key and practical indicators of site locations. These indicators are being easy to access, locating in high-demand areas, such as an urban area, and not being too close to another relief base. These factors were then prioritized using the hierarchical method, and the output indicates the high weight of the factor of “being in a place with high demand”. The objective functions are to maximize coverage, minimize costs, and provide equity in relief time. To deal with the uncertainty of the parameters, we used the robust optimization approach. To initially select potential proposed sites to establish the database, we used Geographic Information System (GIS). To test the above mathematical model in the real world, we conducted a case study in East Tehran. Results: Based on the designed model, the initial points proposed by GIS were identified. Finally, it was found that the number of stations in East Tehran must increase from 27 to 34 bases. Conclusion: By implementing this model, the emergency medical service can provide the highest level of coverage. Also, the maximum relief time at stations will be reduced to 8 minutes.

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Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances

Kaveh

Mesgari

2019

Computers & Industrial Engineering

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Optimising sampling designs for the maximum coverage problem of plume detection

Cited by 4 publications

References 44 publications

Point Divergence Gain and Multidimensional Data Sequences Analysis

Point Divergence Gain and Multidimensional Data Sequences Analysis

Presenting A Multi-objective Location Model in GIS Environment: A Case Study in East Tehran Emergency Stations

Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances

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