Safest and shortest itineraries for transporting hazardous materials using split points of Voronoï spatial diagrams based on spatial modeling of vulnerable zones

Mabrouk, Aziz; Boulmakoul, Azedine; Karim, Lamia; Lbath, Ahmed

doi:10.1016/j.procs.2017.05.311

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…It is effective for municipal waste collection and transportation and has the goal of reducing environmental pollution. Transportation and storage of hazardous materials can cause considerable risk to human life and the environment, due to their toxicity, corrosiveness, explosiveness, and radioactive characteristics (Mabrouk et al, 2017;Paredes-Belmar et al, 2017). In this respect, to design of a remote monitoring system based on a wireless sensor network that can offer real-time information on the vehicle that is transporting the hazardous materials.…”

Section: Maximizing Safety In Hazardous Environments With Ai-driven M...mentioning

confidence: 99%

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Popescu,

Mansoor,

Wani

et al. 2024

Front. Environ. Sci.

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Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.

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Section: Maximizing Safety In Hazardous Environments With Ai-driven M...mentioning

confidence: 99%

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Popescu,

Mansoor,

Wani

et al. 2024

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…The information regarding explosion risk zones or poisonous substance risk zones enables limiting risks and minimizing losses, and consequently protecting people, property and the environment to the highest possible degree against the effects and consequences of the use of hazardous materials (Basu and Verma, 2017). Devising a spatial model enables estimating the proximity of risk-prone areas by calculating the distance separating those objects from tanks, warehouses or routes of vehicles carrying hazardous substances (Mabrouk et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Range of Danger Zones Determined in a Function of LNG Chemical Composition

Kalbarczyk-Jedynak

2018

Multidisciplinary Aspects of Production Engineering

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Nowadays, when we try to automatize all activities, there is a growing demand for energy in all forms. Increasingly we reach for new energy sources that can be problematic to store or to transport, owing to their toxicity or explosive propensity. The article examines the issues of determining danger zones occurring as a result of liquefied natural gas (LNG) release. The range of danger zones caused through LNG release depends on a multitude of factors. The basic parameter that needs to be considered is a type of the released substance as well as the manner of its release. The range of a danger zone is determined by, inter alia, the concentration of a released substance and the atmospheric conditions existing at the time when depressurization occurs. The article analyses the problem of the range of danger zones in a function of wind speed and surface roughness with a defined value of Pasquill stability for various LNG types, starting with pure methane, and ending with the so-called LNG-heavy. The difficulty of the task becomes more complicated when the analysed surface over which a depressurization incident takes place involves water. The problem deepens even further when the analysed substance possesses explosive properties. Then, apart from regular substance concentration, upper and lower flammability limit ought to be considered. Calculations were conducted with DNV-Phast software, version 7.11.

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Distributed Spatio-Temporal Voronoi Diagrams: State of Art and Application to the Measurement of Spatial Accessibility in Urban Spaces

Aggour

Mabrouk

2019

Lecture Notes in Networks and Systems

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Safest and shortest itineraries for transporting hazardous materials using split points of Voronoï spatial diagrams based on spatial modeling of vulnerable zones

Cited by 6 publications

References 13 publications

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

Assessment of the Range of Danger Zones Determined in a Function of LNG Chemical Composition

Distributed Spatio-Temporal Voronoi Diagrams: State of Art and Application to the Measurement of Spatial Accessibility in Urban Spaces

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