Investigating the impact of site management on distress in refugee sites using Fuzzy Cognitive Maps

Drakaki, Maria; Tzionas, Panagiotis

doi:10.1016/j.ijdrr.2021.102282

Cited by 16 publications

(7 citation statements)

References 61 publications

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“…Refugees rely on host governments, international organizations, and non-profit organizations to assist them with access to schooling, medical care, and work among others in the country of destination. When humanitarian aid is not effectively managed, the distress of affected population increases [22,23].…”

Section: Discussionmentioning

confidence: 99%

An Investigation of Time Series Models for Forecasting Mixed Migration Flows: Focusing in Germany

2023

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Refugee and migrant (mixed migration) flows in the Mediterranean have been in the spotlight of both policy and research, especially since 2015. Mixed migration is a volatile international phenomenon with considerable and debatable impacts on society and economy. This paper investigates the performance of time series forecasting methods based on EUROSTAT datasets focusing on asylum seekers. Germany has been selected to reflect on the ability of the models to predict the future behavior of an extremely volatile migrant mobility. Exponential smoothing and autoregressive integrated moving average (ARIMA) models have been used for the forecasting of asylum seekers. Monthly records of first-time asylum seekers have been used from January of 2008 up to September of 2020. The results demonstrate clearly that more research is needed on this field, taking into account the complexity of the characteristics of international migration, in order to assist decision-making in migration management.

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Section: Discussionmentioning

confidence: 99%

An Investigation of Time Series Models for Forecasting Mixed Migration Flows: Focusing in Germany

2023

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“…Recent advances in artificial intelligence in general and machine learning in particular were utilized to enhance how to develop disaster-handling automatic tools [ 1 ]. The new developments recently proposed are also helpful when dealing with man-made crises [ 2 , 3 ]. Recent improvements in machine learning solutions, especially deep learning-based ones, provide a significant boost when developing such tools, thanks to their robust classification capabilities.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Arabic disaster data classification using domain adaptation

Moussa,

Abdou,

Elsayed

et al. 2024

PLoS ONE

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Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results.

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“…Artificial intelligence is a type of machine learning. ML algorithms were pioneers in statistical data analysis and satellite remote sensing thanks to computer vision [2]. They now power a wide range of digital services, including search engines and online shopping.…”

Section: Introductionmentioning

confidence: 99%

“…In any case, applications additionally envelop different sorts information from virtual entertainment presentations on seismic sensor information ganizations and build review records [6]. From capitalizing on our territory to prepar for and recuperating from emergencies, every one of the progressions made in the ut zations of AI can and are being utilized to tackle bigger issues that people face [2]. C By exploiting a significant group of work on picture acknowledgment and characterization, AI is principally applied to techniques that are utilized in the grouping or classification of somewhat detected satellite, elevated, robot, and even street-level symbolism to grasp the fiasco risk.…”

Section: Introductionmentioning

confidence: 99%

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Recent Developments in Machine Learning Predictive Analytics for Disaster Resource Allocation

Pachar,

Dudeja,

Batra

et al. 2023

RAiSE-2023

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Investigating the impact of site management on distress in refugee sites using Fuzzy Cognitive Maps

Cited by 16 publications

References 61 publications

An Investigation of Time Series Models for Forecasting Mixed Migration Flows: Focusing in Germany

An Investigation of Time Series Models for Forecasting Mixed Migration Flows: Focusing in Germany

Enhanced Arabic disaster data classification using domain adaptation

Recent Developments in Machine Learning Predictive Analytics for Disaster Resource Allocation

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