Smart city solid waste management leveraging semantic based collaboration

Ravi, Sai Kishore; Jawahar, Thanga

doi:10.1109/iccids.2017.8272627

Cited by 9 publications

(4 citation statements)

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“…Ravi, Swarnalakshmi et al proposed the concept of a reliable waste management portal that collaborates with other waste collectors and satellite units, for efficient and fast processing of the waste in Smart cites [12]. Waste management authorities use the classification information to on time, notify waste amounts from waste bins.…”

Section: Related Workmentioning

confidence: 99%

Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

2020

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Waste has a direct impact on human health and the surrounding environment. Apart from the health aspect, many industries' growth is effected by waste material such as the food industry. Waste management authorities are interested in reducing the cost of waste management operations and searching for sustainable waste management solutions. For effective planning of waste management, reliable data analysis is required to produce results that can facilitate the planning process. Data mining and machine learning-based data analysis over the waste data can produce a more detailed, and in-time waste information generation, which can lead to effectively manage the waste amount of specific area. In this paper, a descriptive data analysis approach, along with predictive analysis, is used to produce in-time waste information. The performance of the proposed approach is evaluated using a real waste dataset of Jeju Island, South Korea. Waste bins are virtualized on its actual location on the Jeju map in Quantum Geographic Information Systems(QGIS) software. The performance results of the predictive analysis models are evaluated in terms of Mean Absolute Error(MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). Performance results indicate that predictive analysis models are reliable for the effective planning and optimization of waste management operations.INDEX TERMS Waste monitoring, QGIS, descriptive analytics, predictive analytics, waste management, data analysis.

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Section: Related Workmentioning

confidence: 99%

Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

2020

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“…Additionally, organic waste can be treated to generate biogas and biofertilizers. However, due to poor waste management and inadequate collection practices that are currently present in most cities, solid waste causes serious social, health, and environmental problems, as well as economic, resource, and energy losses [4]. Additionally, as a result of poor waste management, the recycling percentage is minimal, and the amount of waste that reaches landfills or areas established for landfills exceeds their capacities, generating a negative impact on society and the world that surrounds us.…”

Section: Introductionmentioning

confidence: 99%

Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia

Vesga Ferreira,

Sepulveda,

Perez Waltero

2024

Sustainability

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Around the world, managing and classifying solid waste is one of the most important challenges to sustaining economic growth and preserving the environment. The objective of this paper is to propose the use of Smart Ecological Points as a strategy to address the problem of solid waste management systems at the source, which has become one of the biggest problems globally, and Colombia is no exception. This article describes the current state of the problem in the country and presents a prototype of a low-cost Smart Ecological Point supported by the use of an experimental capacitive sensor and machine learning algorithms, which will reduce the time necessary for the classification of recyclable and non-recyclable waste, increasing the percentage of waste that can be reused and minimizing health risks by reducing the probability of being contaminated at the source, an aspect that is very common when waste is sorted manually. According to the results obtained, it is evident that the proposed prototype made an adequate classification of waste, generating the possibility of it being manufactured with existing technology in order to promote adequate waste classification at the source.

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“…Solid waste pollution can originate in different places such ashomes, industries, agriculture, shops, hospitals and constructions [10], being the homes the ones that waste the greatest amount of solid waste, representing more than 50% [11], forcing plastics companies to look for solutions to change their conventional manufacturing materials to biodegradable for rapid decomposition [12],allowing humanity, given little concern for recycling, to be solid waste that biodegrades on its own with the help of natural chemical elements [13]. Although it should be noted that the change of manufacturing material by companies from plastics to biodegradable materials is a long process that will take many years to carry out, so the progressive negative impact on the ecosystem will continue to be affected by conventional plastics [14].…”

Section: Introductionmentioning

confidence: 99%

Image Processing System for the Detection of Recyclable Solid Waste

Meneses-Claudio¹,

Muñoz²,

Huamaní³

et al. 2023

IJETAE

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One of the problems that affects everyone is solid waste pollution, which has spread to various parts of the surface and has put the planet in trouble because of the enormous amount of waste that has accumulated over time, affecting the habitats of various marine and terrestrial animals. These damages on the environment, reflect the lack of culture human about recycling, just 10% worldwide recyclemore frequently in homes. Nowadays, the change of materials to biodegradable has been made so that their decomposition is early, accumulations of recyclable materials are still observed in various parts of the surface and observing that it does not solve the contamination. With the exposure of this problem, this article developed an image processing system for the detection of recyclable solid waste that through an automatic analysis will allow the identification of recyclable solid waste materials, such as plastic, cardboard and glass bottles, helping to detect those solid waste that canbe reuse and avoid accumulation of them per ton. Through its development, it was observed that the system identifies the various recyclable materials that are distributed throughout the street with an efficiency of 97.99%, standing out for its efficiency and precision in the analysis of recyclable materials.

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Smart city solid waste management leveraging semantic based collaboration

Cited by 9 publications

References 0 publications

Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia

Image Processing System for the Detection of Recyclable Solid Waste

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