Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators

Izquierdo-Horna, Luis; Damazo, Miker; Yanayaco, Deyvis

doi:10.1016/j.compenvurbsys.2022.101834

Cited by 14 publications

(5 citation statements)

References 47 publications

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“…Izquierdo-Horna et al [25] Using machine learning to identify areas with solid waste accumulation in cities.…”

Section: Authors Description Resultsmentioning

confidence: 99%

“…The proposed method includes waste classification and clustering, simulation of waste-volume forecasts using different models, and evaluation. That approach contributes to optimal waste management and improvement of the environmental situation in the city relative to the closest analogs [22,23,25,26]. The work brings scientific novelty by providing an integrated approach to waste management based on intelligent methods and expands knowledge about optimal waste management strategies to improve the environment and community life.…”

Section: Authors Description Resultsmentioning

confidence: 99%

“…Izquierdo-Horna, L. et al [25] proposed a machine learning method to identify the city sectors prone to solid waste accumulation. The model was built on the 10 basic social indicators (age, education, income, etc.)…”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent Waste-Volume Management Method in the Smart City Concept

Lipianina-Honcharenko,

Komar,

Osolinskyi

et al. 2023

Smart Cities

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This research paper proposes an innovative approach to urban waste management using intelligent methods of classification, clustering, and forecasting. The application of this approach allows for more efficient waste management and contributes to the sustainable development of the urban environment. The aim of this research is to develop an intelligent method for urban waste management, which includes clustering of waste sources, accurate forecasting of waste volumes, and evaluation of forecast results. To achieve this goal, a real dataset with city characteristics and waste data was used. On account of the war in Ukraine, the authors faced the problem of obtaining open data on waste in Ukraine, so it was decided to use data from another city (Singapore). The results show the high efficiency of the developed method. Comparison of the obtained results with the results of the nearest similar works shows that the main feature of this study is the high accuracy of waste-volume forecasting using the XGBoost model, which reached a level of up to 98%.

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“…Izquierdo-Horna et al [25] Using machine learning to identify areas with solid waste accumulation in cities.…”

Section: Authors Description Resultsmentioning

confidence: 99%

Section: Authors Description Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Waste-Volume Management Method in the Smart City Concept

Lipianina-Honcharenko,

Komar,

Osolinskyi

et al. 2023

Smart Cities

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“…A particular case is sodium alginate, a biopolymer that can be found and extracted from seaweeds, which can easily form hydrogels by calcium crosslinking [27] and can be used as an adsorbent of metallic ions [28,29]. Added to the above, the use of biopolymers such as alginate gives the nal material some of the "green" properties, such as low toxicity and suitable biodegradability [30], properties highly important currently considering the important accumulation of solid wastes, in which synthetic polymers obtained from petrochemical sources are one of the main responsables [31,32].…”

Section: Introductionmentioning

confidence: 99%

Marrying plasmonic earth-abundant metals with catalytic metals for visible-light- promoted hydrogen generation on biobased materials

Ramírez

Castillo

Bonardd

et al. 2023

Preprint

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Bimetallic CuPt alloyed nanoparticles were conveniently synthesized on biohydrogels and were capable of carrying out hydrogen release from ammonium borane hydrolysis. The biohydrogel consisted of bead-shaped alginate chains crosslinked by calcium ions, which were used as support material to synthesize and stabilize the bimetallic nanoparticles, employing adsorption and coreduction strategy steps. The as-prepared nanoparticles exhibited light absorption in the visible range (580 nm) resulting from the surface plasmon resonance (SPR) phenomenon ascribed to the presence of copper in the alloyed system. On the other hand, the presence of platinum atoms in these nanoalloys endows them with a notable catalytic performance toward ammonia borane hydrolysis as a hydrogen release reaction, reaching kr values from 0.32 x 10-4 to 2.23 x 10-4 mol L-1 min-1 as the Pt content increases. Finally, by taking advantage of the SPR light absorption shown by CuPt 1:1, it was demonstrated that these entities could be successfully employed as photocatalysts for the hydrogen generation reaction, boosting its activity by almost 2.06 times compared to its performance in dark conditions. This catalytic enhancement was mainly ascribed to the plasmonic-derived light-harvesting properties and the specimen's metallic composition.

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“…En la actualidad, la sociedad se enfrenta a una problemática latente relacionada con la gestión de residuos sólidos (Izquierdo-Horna et al, 2022), la cual ha experimentado un notable aumento en los últimos años, y esto se debe en gran medida al crecimiento poblacional. Como consecuencia, se ha generado un incremento significativo en la producción de residuos sólidos (Debrah et al, 2021).…”

Section: Introductionunclassified

Teledección de residuos sólidos municipales en vertederos a cielo abierto en una provincia de Tacna

Mendoza Aquino,

Mamani Vargas,

Castro Machaca

2023

ING

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La teledetección desempeñó un papel fundamental en el estudio al facilitar la recopilación de datos esenciales para monitorear y crear mapas interactivos que detallan la ubicación de vertederos a cielo abierto de residuos sólidos municipales en la Provincia de Tarata. Este enfoque permitió comprender de manera integral la distribución y características de los vertederos. A través del software ArcMap, se realizó una comparación entre los datos de 2023 y 2016. El proceso implicó examinar imágenes de satélite y registros georreferenciados. Además, se integró un conjunto de ortofotos capturadas por un dron. Los análisis permitieron identificar cuatro vertederos. El más grande se ubicaba en Ticaco, con una superficie de 1568,02 m2, destacando la magnitud del problema de disposición de residuos en esta zona. Por otro lado, el más pequeño se encontraba en Estique, con una extensión de 378 m2. Además, la evaluación de las imágenes reveló la degradación del suelo a lo largo del tiempo, siendo especialmente evidente en la ortofoto capturada por el dron. Esto resalta el impacto ambiental de las actividades de vertedero. En definitiva, el uso de la teledetección y la tecnología de drones proporciona información valiosa para tomar decisiones informadas y gestionar de manera sostenible los residuos en la región.

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Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators

Cited by 14 publications

References 47 publications

Intelligent Waste-Volume Management Method in the Smart City Concept

Intelligent Waste-Volume Management Method in the Smart City Concept

Marrying plasmonic earth-abundant metals with catalytic metals for visible-light- promoted hydrogen generation on biobased materials

Teledección de residuos sólidos municipales en vertederos a cielo abierto en una provincia de Tacna

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